Collecting or triggering collecting positioning data for updating and/or generating a positioning map

Abstract

A method and apparatus are disclosed with the method including obtaining positioning data collection condition information representing positioning data collection condition(s) for collecting positioning data for updating and/or generating a positioning map. The method includes obtaining or holding available historical sensor information representing behavioral and/or environmental pattern(s) that were detected by sensor(s) of a mobile device and determining mapping information representing, for each behavioral and/or environmental pattern, whether or not at least one positioning data collection condition was met. The method also includes obtaining current sensor information representing a behavioral and/or environmental pattern that is currently detected by the sensor(s) and determining, at least partially based on the current sensor information and the mapping information, whether positioning data should be collected. If it is determined that positioning data should be collected, the method includes collecting positioning data by the mobile device for updating and/or generating the positioning map.

Claims

1. A method, said method comprising: obtaining or holding available positioning data collection condition information representing one or more positioning data collection conditions for collecting positioning data for updating and/or generating a positioning map; obtaining or holding available historical sensor information representing one or more behavioral and/or environmental patterns that were detected by one or more sensors of a mobile device; determining or holding available mapping information representing, for each of said behavioral and/or environmental patterns, whether or not at least one of said one or more positioning data collection conditions was met after said respective behavioral and/or environmental pattern had been detected by said sensors of said mobile device; obtaining current sensor information representing a behavioral and/or environmental pattern that is currently detected by said one or more sensors of said mobile device; determining, at least partially based on said current sensor information, said positioning data collection condition information, and said mapping information, whether positioning data should be collected by said mobile device for updating and/or generating the positioning map; when it is determined that positioning data should not be collected by said mobile device for updating and/or generating the positioning map, not collecting or not triggering collecting of positioning data by said mobile device for updating and/or generating the positioning map; and when it is determined that positioning data should be collected by said mobile device for updating and/or generating the positioning map, collecting or triggering collecting of positioning data by said mobile device for updating and/or generating the positioning map.

2. The method according to claim 1, wherein said mapping information represents, for each combination of said behavioral and/or environmental patterns represented by said historical sensor information and said positioning data collection conditions represented by the positioning data collection condition information, a respective probability that said respective positioning data collection condition was met after said respective behavioral and/or environmental pattern had been detected by said sensors of said mobile device.

3. The method according to claim 1, wherein said historical sensor information represents, for each of said behavioral and/or environmental patterns, a respective frequency with which said respective behavioral and/or environmental pattern was detected by said sensors of said mobile device.

4. The method according to claim 1, wherein said positioning data collection condition information represents, for each of said positioning data collection conditions, a respective weighting coefficient.

5. The method according to claim 1, wherein said determining whether positioning data should be collected by said mobile device for updating and/or generating the positioning map comprises: determining a probability for collecting positioning data for updating and/or generating the positioning map.

6. The method according to claim 5, wherein said determining whether positioning data should be collected by said mobile device for updating and/or generating the positioning map further comprises: generating a random or a pseudo-random number having a value between zero and one, wherein, if said random or pseudo-random number is less than or equal to said probability for collecting positioning data for updating and/or generating the positioning map, it is determined that positioning data should be collected by said mobile device for updating and/or generating the positioning map.

7. The method according to claim 1, wherein said sensors of said mobile device detecting said one or more behavioral and/or environmental patterns comprise one or more sensors of the following non-positioning and/or non-radio sensors: an inertial sensor, an acoustic sensor, an optical sensor, a temperature sensor, or a user input sensor.

8. The method according to claim 1, wherein said positioning data collection condition information represents at least one of the following conditions: said mobile device enters a global navigation satellite system (GNSS)-blocked environment; said mobile device exits a GNSS-blocked environment; said mobile device is located in an environment associated with intensive positioning data collection; said mobile device is located in an environment associated with a high density of radio nodes; or said mobile device is located in an environment for which further positioning for updating and/or generating the positioning map should be collected.

9. The method according to claim 1, wherein said positioning data for updating and/or generating the positioning map are at least partially collected by said mobile device by collecting radio fingerprint observation reports.

10. The method according to claim 9, wherein each of said radio fingerprint observation reports indicates a respective observation position and one or more respective radio nodes from which radio signals are observable at said respective observation position by said mobile device.

11. The method according to claim 10, wherein at least one radio node of said one or more respective radio nodes is one of: a Bluetooth beacon; a Bluetooth beacon enabling Bluetooth low energy mode; a Bluetooth low energy beacon; wireless local area network access point or router; and a cellular network node.

12. The method according to claim 1, said method comprising: updating and/or generating or triggering updating and/or generating said positioning map at least partially based on said collected positioning data for updating and/or generating the positioning map.

13. A non-transitory computer readable storage medium in which computer program code is stored, the computer program code when executed by a processor causing at least one apparatus to perform: obtaining or holding available positioning data collection condition information representing one or more positioning data collection conditions for collecting positioning data for updating and/or generating a positioning map; obtaining or holding available historical sensor information representing one or more behavioral and/or environmental patterns that were detected by one or more sensors of a mobile device; determining or holding available mapping information representing, for each of said behavioral and/or environmental patterns, whether or not at least one of said one or more positioning data collection conditions was met after said respective behavioral and/or environmental pattern had been detected by said sensors of said mobile device; obtaining current sensor information representing a behavioral and/or environmental pattern that is currently detected by said one or more sensors of said mobile device; determining, at least partially based on said current sensor information, said positioning data collection condition information, and said mapping information, whether positioning data should be collected by said mobile device for updating and/or generating the positioning map; when it is determined that positioning data should not be collected by said mobile device for updating and/or generating the positioning map, not collecting or not triggering collecting of positioning data by said mobile device for updating and/or generating the positioning map; and when it is determined that positioning data should be collected by said mobile device for updating and/or generating the positioning map, collecting or triggering collecting of positioning data by said mobile device for updating and/or generating the positioning map.

14. The computer readable storage medium according to claim 13, wherein said mapping information represents, for each combination of said behavioral and/or environmental patterns represented by said historical sensor information and said positioning data collection conditions represented by the positioning data collection condition information, a respective probability that said respective positioning data collection condition was met after said respective behavioral and/or environmental pattern had been detected by said sensors of said mobile device.

15. An apparatus comprising at least one processor and at least one memory containing computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause said apparatus at least to: obtain or hold available positioning data collection condition information representing one or more positioning data collection conditions for collecting positioning data for updating and/or generating a positioning map; obtain or hold available historical sensor information representing one or more behavioral and/or environmental patterns that were detected by one or more sensors of a mobile device; determine or hold available mapping information representing, for each of said behavioral and/or environmental patterns, whether or not at least one of said one or more positioning data collection conditions was met after said respective behavioral and/or environmental pattern had been detected by said sensors of said mobile device; obtain current sensor information representing a behavioral and/or environmental pattern that is currently detected by said one or more sensors of said mobile device; determine, at least partially based on said current sensor information, said positioning data collection condition information, and said mapping information, whether positioning data should be collected by said mobile device for updating and/or generating the positioning map; when it is determined that positioning data should not be collected by said mobile device for updating and/or generating the positioning map, not collecting or not triggering collecting of positioning data by said mobile device for updating and/or generating the positioning map; and when it is determined that positioning data should be collected by said mobile device for updating and/or generating the positioning map, collect or trigger collecting of positioning data by said mobile device for updating and/or generating the positioning map.

16. The apparatus according to claim 15, wherein said mapping information represents, for each combination of said behavioral and/or environmental patterns represented by said historical sensor information and said positioning data collection conditions represented by the positioning data collection condition information, a respective probability that said respective positioning data collection condition was met after said respective behavioral and/or environmental pattern had been detected by said sensors of said mobile device.

17. The apparatus according to claim 15, wherein said historical sensor information represents, for each of said behavioral and/or environmental patterns, a respective frequency with which said respective behavioral and/or environmental pattern was detected by said sensors of said mobile device.

18. The apparatus according to claim 15, wherein said positioning data collection condition information represents, for each of said positioning data collection conditions, a respective weighting coefficient.

19. The apparatus according to claim 15, wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause said apparatus to determine whether positioning data should be collected by said mobile device for updating and/or generating the positioning map by determining a probability for collecting positioning data for updating and/or generating the positioning map.

20. The apparatus according to claim 19, wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause said apparatus to determine whether positioning data should be collected by said mobile device for updating and/or generating the positioning map by: generating a random or a pseudo-random number having a value between zero and one, wherein, if said random or pseudo-random number is less than or equal to said probability for collecting positioning data for updating and/or generating the positioning map, it is determined that positioning data should be collected by said mobile device for updating and/or generating the positioning map.

Description

BRIEF DESCRIPTION OF THE FIGURES

(1) FIG. 1a is a block diagram of an exemplary embodiment of a system according to the invention;

(2) FIG. 1b is a block diagram of an exemplary embodiment of an apparatus according to the invention;

(3) FIG. 1c is a block diagram of another exemplary embodiment of an apparatus according to the invention;

(4) FIG. 2 is a flow chart illustrating an exemplary embodiment of a method according to the invention;

(5) FIG. 3 is a schematic illustration of examples of tangible and non-transitory storage media according to the invention.

DETAILED DESCRIPTION OF THE FIGURES

(6) The following description serves to deepen the understanding of the present invention and shall be understood to complement and be read together with the description of example embodiments of the invention as provided in the above SUMMARY section of this specification.

(7) FIG. 1a is a schematic high-level block diagram of a system 100 according to an exemplary aspect of the invention. In the following, it is assumed that system 100 is an indoor radio positioning system for an indoor environment.

(8) The indoor environment is for example inside a building or a complex of buildings like a shopping center, a parking garage, an airport, a company site, etc.

(9) System 100 comprises an indoor radio positioning server 101 and a mobile device 102-1. Optionally, system 100 may further comprise mobile devices 102-2 and 102-3 (i.e. a plurality of mobile devices 102-1 to 102-3) and a plurality of radio nodes 103-1 to 103-3. It is to be understood that system 100 may comprise further mobile devices and radio nodes. In the following, it is thus referred to mobile devices 102-1 to 102-3 and radio nodes 103-1 to 103-3 without limiting the scope of the invention.

(10) For example, each of mobile devices 102-1 to 102-3 may be one of a smartphone, a tablet computer, a notebook computer, a smart watch and a smart band.

(11) It is to be understood that indoor radio positioning system 100 is not limited to a single indoor radio positioning server 101, but may optionally comprise a plurality of servers (e.g. forming a server cloud). Accordingly, the indoor radio positioning server may be part of such a plurality of servers (e.g. a server cloud) or may be represented by such a plurality of servers (e.g. a server cloud).

(12) Radio nodes 103-1 to 103-3 may be fixedly installed in the indoor environment and may be configured to transmit (e.g. broadcast) radio signals. Such a radio signal transmitted by a respective one of radio nodes 103-1 to 103-3 may contain and/or represent positioning support information. The positioning support information are for example configured to enable mobile devices 102-1 to 102-3 receiving the radio signals to estimate their position at least partially based on these positioning support information. For example, the positioning support information may at least represent an identifier of the respective one of radio nodes 103-1 to 103-3 transmitting the radio signal containing this positioning support information. As disclosed above, examples for an identifier of a radio node are a name, an address (e.g. a MAC address or an IP address), an universally unique identifier (UUID), a service set identifier (SSID), a basic service set identifier (BSSID), or a combination thereof.

(13) For example, one or more of radio nodes 103-1 to 103-3 may be a BLE beacon which is configured to automatically and repeatedly transmit BLE radio signals containing positioning support information like an advertisement signal containing and/or representing an UUID of the BLE beacon transmitting the advertisement signal. Alternatively or additionally, one or more of radio nodes 103-1 to 103-3 may be a WLAN access point and/or router which is configured to automatically and repeatedly transmit WLAN radio signals containing positioning support information like a periodically transmitted beacon signal containing and/or representing an SSID of the WLAN of the WLAN access point and/or router transmitting the beacon signal.

(14) In system 100, indoor radio positioning server 101 and mobile devices 102-1 to 102-3 may be configured to communicate with each other as indicated by communication paths 104-1, 104-2 and 104-3, respectively. It is to be understood that communication paths 104-1 to 104-3 may comprise one or more communication links (e.g. one or more wireless communication links or one or more wireline communication links or a combination thereof). Communication paths 104-1 to 104-3 are for example communication paths over a cellular communication system like a 2G/3G/4G/5G cellular communication system. The 2G/3G/4G/5G cellular radio communication standards are developed by the 3GPP and presently available under http://www.3gpp.org/.

(15) Moreover, Indoor radio positioning server 101 may be configured for generating and/or updating an indoor radio positioning map for the indoor environment. For example, the indoor radio positioning map is configured to enable each of the mobile devices 102-1 to 102-3 to estimate its position at least partially based on this indoor radio positioning map when the respective mobile device is located in the indoor environment. For example, the indoor radio positioning map is represented by indoor radio positioning map information which may be transmitted from the indoor radio positioning server 101 to the mobile devices 102-1 to 102-3.

(16) The indoor radio positioning map may be a radio coverage map of the indoor environment which represents at least the expected radio coverage of the radio nodes 103-1 to 103-3 that are installed in the indoor environment. The radio coverage model of such a radio node may describe the area (e.g. the area of the indoor environment) within which a radio signal transmitted or triggered to be transmitted by this radio node is expected to be observable (e.g. receivable with a minimum quality). The real radio coverage of such a radio node may however deviate from the expected radio coverage as described by such a radio coverage model. As disclosed in more detail above, a radio coverage model of a radio node may be a hard-boundary model or a soft-boundary model (e.g. a hard-boundary model or a soft-boundary model describing expected radio coverage).

(17) The indoor radio positioning server 101 may be configured for transmitting indoor radio positioning map information representing the indoor radio positioning map to the mobile devices 102-1 to 102-3 (e.g. via the communication paths 104-1, 104-2 and 104-3, respectively). The mobile devices 102-1 to 102-3 may then use this indoor radio positioning map information for estimating their position based on radio signals received from the radio nodes 103-1 to 103-2 when they are located in the indoor environment.

(18) Mobile devices 102-1 to 102-3 may be configured for collecting radio fingerprint observation reports for updating and/or generating the indoor radio positioning map for the indoor environment and for transmitting the collected radio fingerprint observation reports to the indoor radio positioning server 101 (e.g. via the communication paths 104-1, 104-2 and 104-3, respectively). The indoor radio positioning server 101 may use these radio fingerprint observation reports for generating and/or updating the indoor radio positioning map.

(19) However, collecting radio fingerprint observation reports for updating and/or generating the indoor radio positioning map is a background process that does not directly benefit the users of mobile devices 102-1 to 102-3. To enhance acceptance of this background process by the users, it is thus desirable to limit and/or reduce the usage of resources (e.g. energy consumption for scanning for radio signals, energy consumption for capturing a GNSS position, memory for storing positioning data for updating and/or generating a positioning map, and energy consumption and network bandwidth for data transmission of collected positioning data) of the mobile devices 102-1 to 102-3 by this background process. In particular, it is desirable to avoid that the mobile devices 102-1 to 102-3 permanently collect radio fingerprint observation reports and to provide a method like the a method according to the invention (e.g. the method or parts of the method described below with reference to FIG. 2) that allows determining situationally whether or not radio fingerprint observation reports should be collected by the mobile device and when to start collecting radio fingerprint observation reports.

(20) FIG. 1b is a block diagram of an exemplary embodiment of an apparatus in form of a mobile device 200 according to the invention. In the following, it is assumed that mobile device 200 is one of the mobile devices 102-1 to 102-3 of system 100 of FIG. 1a. For example, mobile device 200 may be one of a smartphone, a tablet computer, a notebook computer, a smart watch and a smart band.

(21) Mobile device 200 comprises a processor 201. Processor 201 may represent a single processor or two or more processors, which are for instance at least partially coupled, for instance via a bus. Processor 201 executes a program code stored in program memory 202 (for instance program code causing mobile device 200 to perform one or more of the embodiments of a method according to the invention or parts thereof (e.g. the method or parts of the method described below with reference to FIG. 2), when executed on processor 201), and interfaces with a main memory 203. Program memory 202 may also contain an operating system for processor 201. Some or all of memories 202 and 203 may also be included into processor 201.

(22) One of or both of a main memory and a program memory of a processor (e.g. program memory 202 and main memory 203 and/or program memory 302 and main memory 303 as described below with reference to FIG. 1c) could be fixedly connected to the processor (e.g. processor 201 and/or processor 301) or at least partially removable from the processor, for instance in the form of a memory card or stick.

(23) A program memory (e.g. program memory 202 and/or program memory 302 as described below with reference to FIG. 1c) may for instance be a non-volatile memory. It may for instance be a FLASH memory (or a part thereof), any of a ROM, PROM, EPROM, MRAM or a FeRAM (or a part thereof) or a hard disc (or a part thereof), to name but a few examples. For example, a program memory may for instance comprise a first memory section that is fixedly installed, and a second memory section that is removable from, for instance in the form of a removable SD memory card.

(24) A main memory (e.g. main memory 203 and/or main memory 303 as described below with reference to FIG. 1c) may for instance be a volatile memory. It may for instance be a DRAM memory, to give non-limiting example. It may for instance be used as a working memory for processor 201 when executing an operating system and/or programs.

(25) Processor 201 further controls a radio interface 204 configured to receive and/or output data and/or information. For instance, radio interface 204 may be configured to receive radio signals from a radio node (e.g. one of radio nodes 103-1 to 103-2 of system 100 of FIG. 1a). The radio interface 204 is configured to scan for radio signals that are broadcast by nodes 103-1 to 103-2 of system 100 of FIG. 1a. Furthermore, the radio interface 204 may be configured for evaluating (e.g. taking measurements on the received radio signals like measuring a received signal strength) and/or extracting data or information from the received radio signals. It is to be understood that any computer program code based processing required for receiving and/or evaluating radio signals may be stored in an own memory of radio interface 204 and executed by an own processor of radio interface 204 or it may be stored for example in memory 202 and executed for example by processor 201.

(26) For example, the radio interface 204 may at least comprise a BLE radio interface including at least a BLE receiver (RX). The BLE receiver may be a part of a BLE transceiver. It is to be understood that the invention is not limited to BLE or Bluetooth. For example, radio interface 204 may additionally or alternatively comprise a WLAN radio interface including at least a WLAN receiver (RX). The WLAN receiver may also be a part of a WLAN transceiver.

(27) Moreover, processor 201 controls a communication interface 205 which is for example configured to communicate according to a cellular communication system like a 2G/3G/4G/5G cellular communication system. Mobile device 200 may use communication interface 205 to communicate with indoor radio positioning server 101 of system 100 (e.g. via one of communication paths 104-1 to 104-3).

(28) Furthermore, processor 201 may control a GNSS positioning sensor 206 (e.g. a GPS and/or a Galileo sensor) and one or more non-radio and/or non-positioning sensors 207. GNSS positioning sensor 206 may be configured to capture a position of the mobile device (e.g. a current position of the mobile device) by receiving GNSS satellite signals of a GNSS system (e.g. GPS satellite signals and/or Galileo satellite signals) and determining the position of the mobile device at least partially based on satellite signals of the GNSS system that are receivable at this position.

(29) As disclosed above, examples for the one or more non-positioning and/or non-radio sensors 207 are: an inertial sensor like an acceleration sensor, a speed or velocity sensor, a gyroscope, a magnetometer, a barometer, etc. an acoustic sensor, an optical sensor, a temperature sensor, a user input sensor (e.g. a user interface like a touch-sensitive display, a keyboard, a touchpad, a display, etc), a clock.

(30) The one or more sensors 207 may be configured to detect, alone or in combination with radio interface 204 and/or GNSS positioning sensor 206, one or more behavioral and/or environmental patterns and to capture sensor information representing the detected one or more behavioral and/or environmental patterns. As disclosed in more detail above and below, a behavioral and/or environmental pattern may be understood to describe a characteristic value of and/or change in at least one parameter captured by at least one sensor of the mobile device, preferably a characteristic value of and/or change in at least two different parameters captured by at least two different sensors of the mobile device. For example, the one or more sensors 207 may be configured to (e.g. permanently or regularly) scan for such (e.g. predetermined or learned) behavioral and/or environmental patterns. In this example, a specific (e.g. predetermined or learned) behavioral and/or environmental pattern may be understood to be detected by the one or more sensors 207 of the mobile device if a characteristic value of and/or change in at least one parameter as described by the specific predetermined or learned behavioral and/or environmental pattern is captured by at least one sensor of the mobile device.

(31) The components 202 to 207 of server 100 may for instance be connected with processor 201 by means of one or more serial and/or parallel busses.

(32) It is to be understood that mobile device 200 may comprise various other components.

(33) FIG. 1c is a block diagram of an exemplary embodiment of an apparatus in form of an indoor radio positioning server 300 according to the invention. In the following, it is assumed that indoor radio positioning server 300 is indoor radio positioning server 101 of system 100 of FIG. 1a.

(34) Indoor radio positioning server 300 comprises a processor 301. Processor 301 may represent a single processor or two or more processors, which are for instance at least partially coupled, for instance via a bus. Processor 301 executes a program code stored in program memory 302 (for instance program code causing indoor radio positioning server 300 to perform one or more of the embodiments of a method according to the invention or parts thereof (e.g. the method or parts of the method described below with reference to FIG. 2), when executed on processor 301), and interfaces with a main memory 303.

(35) Program memory 302 may also comprise an operating system for processor 301. Some or all of memories 302 and 303 may also be included into processor 301.

(36) Moreover, processor 301 controls a communication interface 304 which is for example configured to communicate according to a cellular communication system like a 2G/3G/4G/5G cellular communication system. Indoor radio positioning server 300 may use communication interface 304 to communicate with mobile devices 102-1 to 102-3 of system 100 (e.g. via one of communication paths 104-1 to 104-3).

(37) The components 302 to 304 of indoor radio positioning server 300 may for instance be connected with processor 301 by means of one or more serial and/or parallel busses.

(38) It is to be understood that indoor radio positioning server 300 may comprise various other components. For example, indoor radio positioning server 300 may optionally comprise a user interface (e.g. a touch-sensitive display, a keyboard, a touchpad, a display, etc).

(39) FIG. 2 is a flow chart 400 illustrating an exemplary embodiment of a method according to the invention. Without limiting the scope of the invention, it is assumed in the following that mobile device 102-1 of indoor radio positioning system 100 as described above with respect to FIG. 1a performs steps 401 to 407 of flow chart 400.

(40) In a step 401, positioning data collection condition information representing one or more positioning data collection conditions for collecting positioning data for updating and/or generating a positioning map is obtained or hold available by mobile device 102-1. Therein, it is assumed in the following that the positioning map is the indoor radio positioning map for the indoor environment of indoor radio positioning system 100.

(41) For example, mobile device 102-1 may receive the positioning data collection condition information from indoor radio positioning server 101 via communication paths 104-1 by means of communication interface 205 in step 401. Subsequently, mobile device 102-1 may store the positioning data collection condition information in program memory 202.

(42) Optionally, mobile device 102-1 may receive indoor radio positioning map information representing the indoor radio positioning map for the indoor environment of indoor radio positioning system 100 from indoor radio positioning server 101 via communication paths 104-1 by means of communication interface 205 in step 401. Subsequently, mobile device 102-1 may store the indoor radio positioning map information in program memory 20).

(43) For example, the positioning data collection condition information may be part of the indoor radio positioning map information. Alternatively, the positioning data collection condition information may be separate from the indoor radio positioning map information.

(44) The positioning data collection condition information may be or may represent a database or a bank containing the one or more positioning data collection conditions for collecting positioning data for updating and/or generating the indoor radio positioning map.

(45) The positioning data collection conditions for collecting positioning data for updating and/or generating the indoor radio positioning map for the indoor environment of indoor radio positioning system 100 may be predetermined and may describe conditions under which positioning data should be collected by mobile device 102-1 for updating and/or generating the indoor radio positioning map.

(46) For example, the positioning data collection conditions may specify that positioning data should be collected by the mobile device 102-1 for updating and/or generating the indoor radio positioning map if the mobile device 102-1 enters or exits or is located in an environment for which further positioning data need to be collected to generate or update the indoor radio positioning map. To this end, at least one positioning data collection condition may specify that positioning data should be collected by the mobile device 102-1 for updating and/or generating the indoor radio positioning map if the mobile device 102-1 enters or exits or is located in such an environment for which the indoor radio positioning map is not available or incomplete or outdated. Accordingly, this positioning data collection condition may be understood to be met if it is determined that the mobile device 102-1 enters or exits or is located in an environment for which the positioning map is not available or incomplete or outdated. For example it may be determined that the mobile device 102-1 is located in an such an environment if positioning data captured by GNSS positioning sensor 206 of the mobile device 102-1 indicate that the mobile device 102-1 has entered or is located in such an environment.

(47) Alternatively or additionally, the positioning data collection conditions may specify that positioning data should be collected by the mobile device 102-1 for updating and/or generating the indoor radio positioning map for the indoor environment of indoor radio positioning system 100 if the mobile device 102-1 may enter or exit or be located in an environment which is crucial for enabling positioning based on the indoor radio positioning map like a GNSS-blocked environment or an environment associated with intensive positioning data collection or an environment associated with a high density of radio nodes. To this end, at least one positioning data collection condition may specify that positioning data should be collected by the mobile device 102-1 if the mobile device 102-1 potentially enters or exits such an environment (e.g. a GNSS-blocked environment like an indoor environment). Accordingly, this positioning data collection condition may be understood to be met if it is determined that the mobile device enters or exits or is located in such an environment (e.g. a GNSS-blocked environment like an indoor environment). For example it may be determined that the mobile device is located in an GNSS-blocked area if GNSS positioning sensor 206 of the mobile device fails to obtain sufficient GNSS positioning signals for determining the position of mobile device 102-1.

(48) In particular, the positioning data collection condition information may represent at least one of the following conditions: mobile device 102-1 enters a GNSS-blocked environment; mobile device 102-1 exits an GNSS-blocked environment; mobile device 102-1 is located in an environment associated with intensive positioning data collection; mobile device 102-1 is located in an environment associated with a high density of radio nodes; mobile device 102-1 is located in an environment for which further positioning for updating and/or generating the positioning map should be collected.

(49) Moreover, the positioning data collection condition information may represent a respective weighting coefficient for each of the positioning data collection conditions. Therein, the positioning data collection conditions may be prioritized by the weighting coefficients. For example, a specific weighting coefficient for a specific positioning data collection condition may indicate the importance of collecting positioning data if the specific positioning data collection condition is met. The weighting coefficients may have a value between zero and one.

(50) In the following, it is assumed that the positioning data collection condition information represent three positioning data collection conditions and a respective weighting coefficient for each of the positioning data collection conditions. A representation of this exemplary positioning data collection condition information is shown in the following table, where the positioning data collection conditions are indexed with i and the weighting conditions for the positioning data collections are denoted with col:

(51) TABLE-US-00001 1.sup.st positioning data 2.sup.nd positioning data 3.sup.rd positioning data collection condition collection condition collection condition i 1 2 3 ω.sub.i 0.3 0.5 0.2

(52) It is to be understood that this representation of positioning data collection condition information as shown in the table is exemplary and non-limiting.

(53) In a step 402, historical sensor information representing one or more behavioral and/or environmental patterns that were detected by one or more sensors (e.g. sensors 207) of mobile device 102-1 is obtained or hold available by mobile device 102-1.

(54) As disclosed above, the one or more sensors 207 may be configured to detect, alone or in combination with radio interface 204 and/or GNSS positioning sensor 206, one or more behavioral and/or environmental patterns and to capture sensor information representing the detected one or more behavioral and/or environmental patterns.

(55) For example, the one or more sensors 207 of the mobile device 102-1 may be configured to (e.g. permanently or regularly) scan for (e.g. predetermined or learned) behavioral and/or environmental patterns. Furthermore, sensor information captured by radio interface 204 and/or GNSS positioning sensor 206 for another purpose may for example be freely and/or inexpensively available for detecting behavioral and/or environmental patterns. If a specific (e.g. predetermined or learned) behavioral and/or environmental pattern is detected by the one or more sensors 207, alone or in combination with radio interface 204 and/or GNSS positioning sensor 206, sensor information representing the specific behavioral and/or environmental pattern (e.g. by representing an identifier of the specific behavioral and/or environmental pattern and/or one or more parameters captured by the one or more sensors 207 alone or in combination with radio interface 204 and/or GNSS positioning sensor 206) may be obtained and subsequently stored as or as part of historical sensor information in program memory 202.

(56) In the following, it is assumed that the historical sensor information contains sensor information representing one or more behavioral and/or environmental patterns that were detected by the one or more sensors 207 of the mobile device during a learning phase. Therein, the learning phase is assumed to be the last 10 days. It is to be understood that this configuration is exemplary and non-limiting.

(57) As disclosed above, a behavioral and/or environmental pattern may be understood to describe a value of (e.g. a value range of) and/or a change in at least one parameter captured by at least one sensor of the one or more sensors 207 of mobile device 102-1, preferably a value of (e.g. a value range of) and/or a change in at least two different parameters captured by at least two different sensors of the one or more sensors 207 of mobile device 102-1. Such a value of (e.g. a value range of) and/or change in at least one parameter may be considered to be characteristic for a specific behavior of the user of the mobile device and/or for a specific environment of the mobile device.

(58) The one or more behavioral and/or environmental patterns may be predetermined. Alternatively or additionally, the one or more behavioral and/or environmental patterns may be learned by the mobile device 102-1 during the learning phase (e.g. by use of an algorithm configured to identify behavioral and/or environmental patterns from changes in at least one parameter (e.g. repeatedly) captured by at least one sensor of the mobile device like a machine learning algorithm).

(59) Examples for such (e.g. predetermined or learned) behavioral and/or environmental patterns are: Speed and/or motion state or change in speed and/or motion state (e.g. captured by an acceleration sensor or speed or velocity sensor of sensors 207). For example, a change of speed and/or motion state from driving a car to walking may indicate that it is likely that the user of the mobile device 102-1 has arrived at an indoor environment by car and will soon enter the indoor environment. The current time of day or time of week (e.g. provided by a dock) is such that one or more positioning data collection conditions were frequently met at or dose to the same time of day or time of week during the learning phase. Launching a certain application (e.g. captured by a user interface sensor of sensors 207), for example an application for the indoor environment represented by the radio indoor positioning map of indoor radio positioning system 100. This may for example indicate that it is likely the mobile device 102-1 (or the user of the mobile device) is located in or wil soon enter the indoor environment represented by the radio indoor positioning map of indoor radio positioning system 100. Environmental parameters like environment temperature and/or lighting and/or volume change (e.g. captured by a temperature and/or an optical and/or an acoustical sensor of sensors 207). A (e.g. rapid) change in such environmental parameters may for example indicate that it is likely that the mobile device 102-1 (or the user of the mobile device) enters or exits an indoor environment. The current position of the mobile device 102-1 (e.g. captured by GNSS sensor 206) indicates that the mobile device 102-1 is in or close to an environment where a positioning data collection condition was frequently met during the learning phase or that the mobile device 102-1 is located in or close to an environment associated with intensive positioning data collection. Therein, the current position may not be captured for the purpose of detecting a behavioral or environmental pattern, but the position may be captured for another purpose (e.g. on request of the user or an (e.g. user) application of the mobile device 102-1) such that the position can also be used (e.g. reused) for detecting a behavioral or environmental pattern. Since no or only very limited additional resources are consumed when the current position of the mobile device 102-1 captured for another purpose is also used (e.g. reused) for detecting a behavioral or environmental pattern, this current position of the mobile device 102-1 may be understood to be obtained inexpensively or for free for detecting a behavioral or environmental pattern. The radio signals (e.g. captured by radio interface 204) that are observable at the current position of the mobile device 102-1 indicate that the mobile device 102-1 is close to one or more radio nodes which are located in or close to an environment where a positioning data collection condition was frequently met during the learning phase or that the mobile device 102-1 is located in or close to an environment associated with a high density of radio nodes. Therein, the radio signals may not be captured for the purpose of detecting a behavioral or environmental pattern, but the radio signals may be captured for another purpose (e.g. on request of the user or an (e.g. user) application of the mobile device 102-1) such that the radio signals can also be used (e.g. reused) for detecting a behavioral or environmental pattern. Since no or only very limited additional resources are consumed when such radio signals captured for another purpose are also used (e.g. reused) for detecting a behavioral or environmental pattern, these radio signals may be understood to be obtained inexpensively or for free for detecting a behavioral or environmental pattern. A combination of these behavioral or environmental patterns. For example, the speed and/or motion state (e.g. captured by an acceleration sensor or speed or velocity sensor of sensors 207) is running and the current location (e.g. captured by GNSS positioning sensor 206) is in or close to an environment where a positioning data collection condition was frequently met during the learning phase.

(60) Moreover, the historical sensor information may represent, for each behavioral or environmental pattern, a frequency with which the respective behavioral and/or environmental pattern was detected by the sensors of the mobile device during the learning phase. The frequency with which a specific behavioral and/or environmental pattern was detected by the sensors of the mobile device during the learning phase may for example be the absolute frequency or the relative frequency or the frequency per predetermined time period (e.g. per day or per hour) with which a specific behavioral and/or environmental pattern was detected by the sensors of the mobile device during the learning phase.

(61) In the following, it is assumed that the historical sensor information represent four behavioral or environmental patterns that were detected by the sensors of the mobile device during the learning phase in the last ten days and a respective frequency per day for each of these four behavioral or environmental patterns. The first behavioral or environmental pattern was for example detected every day, the second behavioral or environmental pattern was for example detected on 2 days, the third behavioral or environmental pattern was for example detected on 7 days and the fourth behavioral or environmental pattern was for example detected on 5 days. A representation of this exemplary historical sensor information is shown in the following table, where the behavioral or environmental patterns are indexed with j and the frequencies for the positioning data collections are denoted with f.sub.j:

(62) TABLE-US-00002 1.sup.st behavioral or 2.sup.nd behavioral or 3.sup.rd behavioral or 4.sup.th behavioral or environmental environmental environmental environmental pattern pattern pattern pattern j 1 2 3 4 f.sub.j $\frac{10}{10} = 1$ $\frac{2}{10} = 0.2$ $\frac{7}{10} = 0.7$ $\frac{5}{10} = 0.5$

(63) It is to be understood that this representation of historical sensor information as shown in the table is exemplary and non-limiting.

(64) In a step 403, mapping information representing, for each of the behavioral and/or environmental patterns, whether or not at least one of the one or more positioning data collection conditions was met after the respective behavioral and/or environmental pattern had been detected by the sensors (e.g. sensors 207) of mobile device 102-1 are hold available and/or determined.

(65) For example, the mapping information may be determined by determining, for each of the behavioral and/or environmental patterns that were detected during the learning phase and that are represented by the historical sensor information obtained or hold available in step 402, whether or not at least one of the one or more positioning data collection conditions (represented by the positioning data collection condition information obtained or hold available in step 401) was met after the respective behavioral and/or environmental pattern had been detected by sensors 207 (e.g. alone or in combination with radio interface 204 and/or GNSS positioning sensor 206) of the mobile device 102-1 during the learning phase. For example, the mapping information may be obtained as a result of this determining. Subsequently, the mapping information may be stored in program memory 202 of mobile device 102-1.

(66) That at least one of the one or more positioning data collection conditions was met after the respective behavioral and/or environmental pattern had been detected may be understood to mean that the at least one of the one or more positioning data collection conditions was met within a predetermined time period (e.g. 5 minutes, or 1 minute, or 30 seconds) after the respective behavioral and/or environmental pattern had been detected.

(67) In the following, it is assumed that the mapping information represents, for each combination of the behavioral and/or environmental patterns represented by the historical sensor information and the positioning data collection conditions, a respective probability that the respective positioning data collection condition was met after the respective behavioral and/or environmental pattern had been detected by the sensors of the mobile during the learning phase.

(68) Considering the above examples, the positioning data collection condition information represents three positioning data collection conditions and the historical sensor information represents four behavioral and/or environmental patterns that were detected during the learning phase in the last 10 days such that there are 12 possible combinations. The following table represents how often the four behavioral and/or environmental pattern were detected during the learning phase and how often the positioning data collection conditions were met after one of the behavioral and/or environmental patterns had been detected. Therein, a detected behavioral and/or environmental pattern and the positioning data collection condition met after this behavioral and/or environmental pattern had been detected are written in the same row. For example, on day 1: the 2.sup.nd positioning data collection condition was met after the 1.sup.st behavioral and/or environmental patterns had been detected; and the 2.sup.nd positioning data collection conditions was met after the 3.sup.rd behavioral and/or environmental patterns had been detected.

(69) TABLE-US-00003 Met positioning data collection condition after Detected behavioral and/or detecting behavioral and/or environmental pattern environmental pattern Day 1 1.sup.st 2.sup.nd 3.sup.rd 2.sup.nd Day 2 1.sup.st 2.sup.nd 3.sup.rd 2.sup.nd Day 3 1.sup.st — 2.sup.nd — 3.sup.rd 2.sup.nd Day 4 1.sup.st — 4.sup.th 1.sup.st Day 5 1.sup.st 2.sup.nd 2.sup.nd — 3.sup.rd 2.sup.nd Day 6 1.sup.st — 4.sup.th — Day 7 1.sup.st 2.sup.nd 3.sup.rd 2.sup.nd Day 8 1.sup.st — 3.sup.rd 2.sup.nd 4.sup.th — Day 9 1.sup.st — 4.sup.th 1.sup.st Day 10 1.sup.st 2.sup.nd 3.sup.rd 2.sup.nd 4.sup.th 3.sup.rd

(70) Accordingly, the probabilities for each of the possible combinations as represented by the mapping information may be represented by the following table, where the positioning data collection conditions are indexed with i, the behavioral or environmental patterns are indexed with J and the probabilities for the combinations are denoted with p.sub.i|j: (i.e. probability that i-th positioning data collecting condition was met after j-th behavioral or environmental pattern had been detected):

(71) TABLE-US-00004 1.sup.st behavioral or 2.sup.nd behavioral or 3.sup.rd behavioral or 4.sup.th behavioral or environmental environmental environmental environmental p.sub.i|j pattern (j = 1) pattern (j = 2) pattern (j = 3) pattern (j = 4) 1.sup.st positioning data collecting condition (i = 1) $p_{1 | 1} = \frac{0}{10} = 0$ $p_{1 | 2} = \frac{0}{2} = 0$ $p_{1 | 3} = \frac{0}{7} = 0$ $p_{1 | 4} = \frac{2}{4} = 0.5$ 2.sup.nd positioning data collecting condition (i = 2) 0 $p_{2 | 1} = \frac{5}{10} = 0.5$ $p_{2 | 2} = \frac{0}{2} = 0$ $p_{2 | 3} = \frac{7}{7} = 1$ $p_{2 | 4} = \frac{0}{4} = 0$ 3.sup.rd positioning data collecting condition (i = 3) $p_{3 | 1} = \frac{0}{10} = 0$ $p_{3 | 2} = \frac{0}{2} = 0$ $p_{3 | 3} = \frac{0}{7} = 0$ $p_{3 | 4} = \frac{1}{4} = 0.25$

(72) It is to be understood that this representation of mapping information as shown in the table is exemplary and non-limiting.

(73) Steps 401 to 403 may be part of the learning phase. Accordingly, mapping from detected behavioral or environmental patterns to positioning data collection conditions are learned during the learning phase. The learning takes place based on behavioral or environmental patterns detected by the mobile device 102-1. Nevertheless, it may be considered to be not only device-specific, but also user-specific, because typically a mobile device is only used by a single user. Furthermore, the learning phase may be performed for different users of mobile device 102-1 separately.

(74) When the learning phase is completed, the information (e.g. the historical sensor information or the mapping information) collected and/or learned during the learning phase may be used to predict the likelihood that a positioning data collection condition will be met when a behavioral or environmental pattern is detected. It is to be understood that the learning phase (i.e. steps 401 to 403) may be performed continually by mobile device 102-1. However, the use of resources of mobile device 102-1 may be adjusted. For example, when mobile device 102-1 is used for the first time, default values (e.g. for the above disclosed probabilities and frequencies) may be used. During the learning phase, these default values get adjusted as disclosed above. To facilitate this adjustment, it may be beneficial to use resources of mobile device 102-1 more intensively in the beginning (e.g. during the initial learning phase) and to reduce the use of resources of mobile device 102-1 thereafter.

(75) In a step 404, current sensor information representing a behavioral and/or environmental pattern that is currently detected by the one or more sensors (e.g. sensors 207) the mobile device 102-1 are obtained by mobile device 102-1. For example, the current sensor information representing the behavioral and/or environmental pattern that is currently detected may be understood to represent the specific behavioral and/or environmental pattern that was detected last or recently (e.g. within a predetermined time period, e.g. within the last 5 minutes, or 1 minute, or 30 seconds) by the sensors 207 (e.g. alone or in combination with radio interface 204 and/or GNSS positioning sensor 206) of mobile device 102-1. The current sensor information may not be part of the historical sensor information.

(76) In a step 405, it is determined, at least partially based on the current sensor information obtained in step 404 and the mapping information determined or hold available in step 403, whether positioning data should be collected by mobile device 102-1 for updating and/or generating the indoor radio positioning map for the indoor environment of indoor radio positioning system 100.

(77) In the following, it is assumed that the determining in step 405 whether positioning data should be collected by the mobile device for updating and/or generating the positioning map comprises (1) computing a probability for collecting positioning data for updating and/or generating the indoor radio positioning map and (2) generating a random or a pseudo-random number having a value between zero and one, wherein, if the random or pseudo-random number is less or equal the probability for collecting positioning data for updating and/or generating the positioning map, it is determined that positioning data should be collected by the mobile device for updating and/or generating the positioning map.

(78) The probability for collecting positioning data for updating and/or generating the indoor radio positioning map may be computed at least partially based on the information collected or learned during the learning phase (i.e. In steps 401 to 403 as disclosed above). In particular, the above disclosed (1) probabilities p.sub.i|j for the combinations of the behavioral and/or environmental patterns and the positioning data collection conditions, (2) frequencies f.sub.j for the behavioral and/or environmental patterns and (3) weighting coefficients w, for the positioning data collection conditions may be considered as input values when computing a probability for collecting positioning data for updating and/or generating the indoor radio positioning map. Furthermore, computing the probability for collecting positioning data for updating and/or generating may be a function of the behavioral and/or environmental pattern represented by the current sensor information obtained in step 404. For example, the probability for collecting positioning data for updating and/or generating the indoor radio positioning map may be computed based on the following formula:

(79) $ℙ_{j} = \frac{Σ_{.Math.} ω_{i} .Math. p_{i | j}}{Σ_{i} ω_{i} (p_{i | j} + Σ_{k \neq j} p_{i | k} f_{k})}$

(80) With this formula, the probability custom character .sub.j for collecting positioning data for updating and/or generating the positioning map, which is computed for the j-th behavioral and/or environmental pattern currently detected and, thus, represented by the current sensor information, is proportional to the expected importance or priority Σ.sub.iω.sub.i.Math.p.sub.i|j. However, if the probability of detecting any other behavioral and/or environmental pattern in combination with a positioning data collection condition during the same predetermined time period (p.sub.i|kf.sub.k for the k-th behavioral and/or environmental pattern and the i-th positioning data collection condition based on basic probability calculus) and the expected importances given by the other behavioral and/or environmental patterns Σ.sub.iω.sub.i.Math.p.sub.i|k.Math.f.sub.k increase, the probability custom character .sub.j for collecting positioning data for updating and/or generating the positioning map decreases.

(81) Considering the above example values for p.sub.i|j, f.sub.j, ω.sub.i and when the 3.sup.rd behavioral and/or environmental pattern (i.e. j=3) is represented by the current sensor information obtained in step 404, the probability for collecting positioning data for updating and/or generating the positioning map is computed as follows:

(82) $ℙ_{3} = \frac{0.3 .Math. 0 + 0.5 .Math. 1 + 0.2 .Math. 0}{\begin{matrix} 0.3 (0 + 0 .Math. 1 + 0 .Math. 0.2 + 0.5 .Math. 0.5) + \\ 0.5 (1 + 0.5 .Math. 1 + 0 .Math. 0.2 + 0 .Math. 0.5) + \\ 0.2 (0 + 0 .Math. 1 + 0 .Math. 0.2 + 0.25 .Math. 0.5) \end{matrix}} = 0.59$

(83) When the 1.sup.st behavioral and/or environmental pattern (i.e. j=1) is detected, the probability for collecting positioning data for updating and/or generating the positioning map is custom character .sub.1=0.36. In this regard, it is noted that .sub.1 is about 61% of .sub.3 even though the probability that the 2.sup.nd positioning data collection condition is met when the 1.sup.st behavioral and/or environmental pattern is detected is only half of probability that the 2.sup.nd positioning data collection condition is met when the 3.sup.rd behavioral and/or environmental pattern is detected. This is explained by the fact that even when the 3.sup.rd behavioral and/or environmental pattern is detected, the 1.sup.st behavioral and/or environmental pattern is likely to follow at the same day, which makes collecting positioning data less important when the 3.sup.rd behavioral and/or environmental pattern is detected.

(84) The above disclosed formula contains the approximation that each behavioral and/or environmental pattern is detected once a day and is independent of the time of day. It is to be understood that the invention is not limited to this formula. For example, formulas that consider also multiple detections per day, for example by including specific frequencies per hour and/or by accounting for earlier detections at the same day, may also be used. In particular, this kind of formulas may be necessary when the behavioral and/or environmental pattern depend on time (e.g. time of day). For example, probabilities f.sub.j may be multiplied with the percentage of the current day remaining to take such a time dependency into consideration.

(85) After computing the probability custom character .sub.j for collecting positioning data for updating and/or generating the positioning map, a pseudo-random number u may be generated from a continuous uniform distribution between 0 for and 1. If u≤.sub.j (or, alternatively, if u<.sub.j), it is determined in step 405 that positioning data should be collected by the mobile device for updating and/or generating the positioning map. Otherwise, it may be determined that that positioning data should not be collected by the mobile device for updating and/or generating the positioning map.

(86) If it is determined in step 405 that positioning data should not be collected by the mobile device for updating and/or generating the positioning map, the flow chart is terminated in step 406. Otherwise, if it is determined in step 405 that positioning data should be collected by the mobile device for updating and/or generating the positioning map, the flowchart continues with a step 407.

(87) In step 407, positioning data for updating and/or generating the positioning map are collected by mobile device 102-1, or mobile device 102-1 is triggered to collect positioning data for updating and/or generating the positioning map.

(88) For example, mobile device 102-1 may collect positioning data in step 407 by collecting radio fingerprint observation reports, for example by generating the radio fingerprint observation reports at least partially based on scanning results. For example, collecting a radio fingerprint observation report by mobile device 102-1 may comprise scanning for radio signals observable at an observation position of the mobile device (e.g. by radio interface 204) and generating a radio fingerprint observation report containing an indication for the radio nodes from which a radio signal is observable at the observation position of the mobile device and an indication of the observation position of the mobile device. A radio signal may be understood to be observable at an observation position of the mobile device if the radio signal is receivable with a minimum quality (e.g. a minimum signal-to-noise ratio and/or a minimum signal power) at this position.

(89) Mobile device 102-1 may transmit the collected radio fingerprint observation reports via communication paths 104-1 to indoor radio positioning server 101. The indoor radio positioning server 101 may use the collected radio fingerprint observation reports for updating and/or generating the indoor radio positioning map.

(90) To summarize, the invention inter-alia provides a mechanism for situationally determining whether or not positioning data should be collected by the mobile device. It can thus for example be avoided that mobile device 102-1 permanently collects positioning data such that the usage of resources for collecting positioning data is limited.

(91) Moreover, the invention allows the mobile device 102-1 to start to collect positioning data for updating and/or generating the positioning map before a positioning data collection condition is met, but when it is likely that the positioning data collection condition will be met soon. This is for example advantageous over prior art implementations where the collection of positioning data is started when it is determined that the positioning data collection condition is met, because often it is already too late to start collecting positioning data when the it is determined that the positioning data collection condition is met. For example, when the mobile device 102-1 determines that it is located in an indoor environment for which further positioning data need to be collected to generate or update the positioning map, it may be too late to start collecting positioning data, because it is already impossible to get a GNSS-based initial position for sensor-based indoor positioning. Another example is that positioning data should not always be collected when a less important or less prioritized positioning data collection condition is met or may be met if it is likely that even more important or higher prioritized positioning data collection condition will occur soon. Therein, Inexpensive measurements such as motion state detection (with low-frequency inertial measurements), phone usage data such as used or launched applications, temperature, time, freely available sensor information captured by a radio or positioning sensor, possibly cellular positioning, etc. may be used.

(92) It is to be understood that the orders of the steps 401 to 407 of flowchart 400 is only exemplary and that the steps may also have a different order if possible. Furthermore, it is also possible that two or more steps may be performed in one step.

(93) FIG. 3 is a schematic illustration of examples of tangible and non-transitory computer-readable storage media according to the present invention that may for instance be used to implement program memory 202 of FIG. 1b or memory 302 of FIG. 1c. To this end, FIG. 3 displays a flash memory 500, which may for instance be soldered or bonded to a printed circuit board, a solid-state drive 501 comprising a plurality of memory chips (e.g. Flash memory chips), a magnetic hard drive 502, a Secure Digital (SD) card 503, a Universal Serial Bus (USB) memory stick 504, an optical storage medium 505 (such as for instance a CD-ROM or DVD) and a magnetic storage medium 506.

(94) Any presented connection in the described embodiments is to be understood in a way that the involved components are operationally coupled. Thus, the connections can be direct or indirect with any number or combination of intervening elements, and there may be merely a functional relationship between the components.

(95) Further, as used in this text, the term ‘circuitry’ refers to any of the following:

(96) (a) hardware-only circuit implementations (such as implementations in only analog and/or digital circuitry)

(97) (b) combinations of circuits and software (and/or firmware), such as: (i) to a combination of processor(s) or (i) to sections of processor(s)/software (including digital signal processor(s)), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone, to perform various functions) and

(98) (c) to circuits, such as a microprocessor(s) or a section of a microprocessor(s), that re-quire software or firmware for operation, even if the software or firmware is not physically present.

(99) This definition of ‘circuitry’ applies to all uses of this term in this text, including in any claims. As a further example, as used in this text, the term ‘circuitry’ also covers an implementation of merely a processor (or multiple processors) or section of a processor and its (or their) accompanying software and/or firmware. The term ‘circuitry’ also covers, for example, a baseband integrated circuit or applications processor integrated circuit for a mobile phone.

(100) Any of the processors mentioned in this text, in particular but not limited to processors 201 and 301 of FIGS. 1b and 1c, could be a processor of any suitable type. Any processor may comprise but is not limited to one or more microprocessors, one or more processor(s) with accompanying digital signal processor(s), one or more processor(s) without accompanying digital signal processor(s), one or more special-purpose computer chips, one or more field-programmable gate arrays (FPGAS), one or more controllers, one or more application-specific integrated circuits (ASICS), or one or more computer(s). The relevant structure/hardware has been programmed in such a way to carry out the described function.

(101) Moreover, any of the actions or steps described or illustrated herein may be implemented using executable instructions in a general-purpose or special-purpose processor and stored on a computer-readable storage medium (e.g., disk, memory, or the like) to be executed by such a processor. References to ‘computer-readable storage medium’ should be understood to encompass specialized circuits such as FPGAs, ASICs, signal processing devices, and other devices.

(102) The wording “A, or B, or C, or a combination thereof” or “at least one of A, B and C” may be understood to be not exhaustive and to include at least the following: (i) A, or (ii) B, or (iii) C, or (iv) A and B, or (v) A and C, or (vi) B and C, or (vii) A and B and C.

(103) It will be understood that all presented embodiments are only exemplary, and that any feature presented for a particular exemplary embodiment may be used with any aspect of the invention on its own or in combination with any feature presented for the same or another particular exemplary embodiment and/or in combination with any other feature not mentioned. It will further be understood that any feature presented for an example embodiment in a particular category may also be used in a corresponding manner in an example embodiment of any other category.

Collecting or triggering collecting positioning data for updating and/or generating a positioning map

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G01S5/02525

PHYSICS

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G01S5/02526

PHYSICS

International classification

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H04L67/12

ELECTRICITY

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G01S5/02

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Abstract

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