Technique and system of positioning a mobile terminal indoors

Abstract

The technique and the system may be used for indoor positioning, where signals of navigation satellites are not available. In accordance with the technique patterns identifying location of the mobile terminal in a specific position may be detected, on the basis of data acquired from at least one inertial and non-inertial sensors in the process of movement of at least one mobile terminal; the path of movement of the above mobile terminal may be detected and saved in the local coordinate system associated with the above position, as well as data acquired from non-inertial sensors; statistically averaged parameters of conversion of local coordinate system of the mobile terminal may be generated in the positions detected in the process of terminal movement; at least one map of distribution of output values of non-inertial sensors may be prepared on the basis of data acquired at the previous step; the position of the above mobile terminal may be detected on the basis of data acquired at the previous step. The system may include a set of sensors of mobile terminal, a computer, a probability computation module, a module for selection of patterns, a data storage package and a coordinate converter.

Claims

1. A method for mobile terminal indoor positioning, comprising the following steps: detecting patterns identifying a location of the mobile terminal in a specific position on the basis of data obtained from at least one inertial and non-inertial sensor along a movement path of at least one mobile terminal for a plurality of specific positions; detecting and saving of the mobile terminal movement path in a local coordinate system associated with the above for at least some of each specific position and data acquired from non-inertial sensors; determining statistically averaged parameters of conversion between the mobile terminal local coordinate systems for at least some of the specific positions detected along the terminal movement path; preparing at least one map of a distribution of output data from the non-inertial sensor on the basis of the statistically averaged parameters; and estimating a position of the mobile terminal on the basis of the prepared map; wherein the position is estimated with a particle-filter.

2. The method of claim 1, wherein the determination of statistically averaged parameters of conversion between the local coordinate systems for different pairs of patterns detected along the movement path of at least one mobile terminal is repeated one and more times.

3. The method of claim 2 wherein at least one map of distribution of output data is generated, for this purpose coordinates of positions estimated in the local coordinate systems obtained in the process of movement of at least one terminal is re-computed into a global coordinate system.

4. The method of claim 3 wherein a first building layout may be obtained, for this purpose local positions may be recomputed into global ones with due account for restrictions imposed by the layout.

5. The method of any one of claims 1, 2 and 3, wherein more than one pattern identifying location of the mobile terminal in a specific position is detected, further comprising updating the estimation of mobile terminal position.

6. The method of any one of claims 1, 2 and 3, wherein the pattern identifying location of the mobile terminal in a specific position is a sequence of measurements over the specific time interval.

7. The method of claim 1 wherein a number of local coordinate systems is less than a number of identified patterns.

8. The method of claim 7 wherein positions associated with similar patterns are combined into a shared area.

9. The method of claim 8 wherein the mobile terminal is at least one of a smartphone, a tablet computer, a notebook, and motor vehicle navigator.

10. The method of claim 1 wherein the mobile terminal is a device capable to supply information and move together with a user.

11. The method of claim 1 wherein the inertial sensor is a gyroscope or an accelerometer.

12. The method of claim 1 wherein the non-inertial sensor is at least one of a magnetometer, a compass, a barometer, a video-sensor, a microphone, and a radio-signal receiver.

13. A mobile terminal indoor positioning device, comprising: one or more instruction units; one or more data storage devices; one or more programs, wherein the one or more programs are stored on one or more data storage devices and are implemented with one or more processors, wherein the one or more programs comprise instructions for: detecting patterns identifying a location of a mobile terminal in a specific position on the basis of data acquired from inertial and non-inertial sensors along a movement path of at least one mobile terminal for a plurality of specific positions: detecting and saving the mobile terminal movement path in a local coordinate for at least some of each specific position, as well as data acquired from non-inertial sensor; determining statistically averaged parameters of conversion between the mobile terminal local coordinate systems for at least some of the specific positions detected along the terminal movement path; preparing at least one map of distribution of output data acquired from the non-inertial sensors on the basis of the statistically averaged parameters; and estimating a position of the mobile terminal on the basis of the prepared map data; wherein the position is estimated with a particle-filter.

14. A mobile terminal indoor positioning system, comprising: at least one computer system; at least one coordinate converter associated with the above computer system; one and more data storages associated with at least one coordinate converter; at least one module for detection of patterns associated with one and more data storages stated above, wherein each module for detection of patterns comprises at least one non-inertial sensor and at least one inertial sensor; and a module for computation of probability of location of each module for detection of patterns in a specific position associated with the above computer system; wherein the module for detection of patterns detects patterns identifying a location of the mobile terminal in a plurality of specific positions along a movement path of the module of detection of patterns on the basis of data obtained from the inertial and non-inertial sensors, wherein the module for computation of probability of location detects and saves in the one or more data storages the mobile terminal movement path in a local coordinate system for at least some of each specific position and data acquired from non-inertial sensors, wherein the at least one coordinate converter determines statistically averaged parameters of conversion between the local coordinate systems for at least some of the specific positions detected along the movement path and wherein the module for computation of probability of location further prepares at least one map of a distribution of output data from the non-inertial sensor on the basis of the statistically averaged parameters and estimates a position of the module for detection of patterns on the basis of the prepared map; wherein the position is estimated with a particle-filter.

15. The system of claim 14 wherein one and more data storages and at least one coordinate converter are located at one of a remote server and cloud resource.

16. The system of claim 14 wherein communication between at least one computer system and at least one module for detection of patterns is implemented via wireless communication.

17. The system of claim 14 wherein communication between system components is implemented via wireless communication.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) FIG. 1 shows the process of movement of mobile terminal 191 along continuous path 190 in indoor environment 192 to be mapped. In time moments when mobile terminal 191 was located with high degree of confidence in unknown but very well identified positions 101 and 103, directions of its movement have been detected, as well as sections of the path prior and following passage of these positions. In the process of one of such passages, the mobile terminal was moved along path 110 across position 101 in direction 111, and subsequently within the same path section across position 103 in direction 112. In the process of the other passage the mobile terminal was moved along path 120, first across position 103 in direction 122 and then across position 101 in direction 121. In the process of some more passages, the mobile terminal was moved across position 101 along path 130 in direction 131 and along path 140 in direction 141. These path sections do not contain passage across position 103. In the process of one more passage, the mobile terminal was moved across position 103 along path 150 in direction 152. Directions 111, 121, 131, 141, in which the mobile terminal was moved across position 101, as well as mobile terminal movement paths 130, 140, are described in local coordinate system 102 associated with position 101. Directions 112, 122, 152, in which the mobile terminal was moved across position 103, as well as mobile terminal movement path 150, are described in the local coordinate system 104 associated with position 103. Paths 110 and 120 are described both in coordinate system 102 and coordinate system 104.

(2) FIG. 2 explains the patterning process. Path 220 specifying mobile terminal movement in indoor location 221 is shown. Positions 207 and 208, coordinate systems associated with them, 205 and 206, as appropriate, movement directions 209 and 210 are shown. Timeline 200 is shown. Position 207 is shown as unknown position P1 (202) identified with high degree of confidence, position 208as unknown position P2 (204) identified with high degree of confidence. The mobile terminal identifies its visit to position P1 in time moment t.sub.1 (211) and to position P2 in time moment t.sub.2 (212). Patterns are measured at these time moments at the output of mobile terminal sensors, as appropriate, C(t.sub.1) identified with 201 and C(t.sub.2) identified with 203. Braces 213 and 214 show sections of mobile terminal movement path in the time interval including pattern detection moment, that is moments t.sub.1 (211) and t.sub.2 (212).

(3) FIG. 3 explains the process of conversion of local coordinate systems. Path 301, along which the mobile terminal moves, is shown. Locations of the mobile terminal are shown in the successive time moments. Appropriate positions are identified with P1 (304) and P2 (305). Local (Cartesian) coordinate systems 310 and 311 with their centers located in positions P1 and P2, as appropriate, are shown as well. Axes of coordinate system 310 are identified with x (312) and y (313), coordinate system 311 are identified with x (314) and y (315). Parameters for change of the origin of coordinates of the second local system in relation to the first one are identified with a (320) and b (321), the angle of rotation of the second local system in relation to the first onewith (323).

(4) FIG. 4 shows the flow diagram of mobile terminal indoor positioning. The system consists of a plurality of mobile terminals 401 with sensors, computer 402 and probability estimation module 403. In addition, the system includes patterning module 410, data storage 411 and coordinate converter 412.

(5) FIG. 5 explains utilization of data storages in mobile terminal positioning system. Sections of mapped indoor environment are shown in the form of three areas: area 503 with local coordinate system 502 associated with well identified position 501, area 506 with local coordinate system 505 associated with well identified position 504, area 509 with local coordinate system 508 associated with well identified position 507. Orientation of local coordinate systems 502, 505 and 508 may be definitely detected in global coordinate system 510. Areas 503, 506 and 509 define approximate differentiation between locally mapped sections of indoor environment. At that the data related to local coordinate system 502 are filed in storage 511, the data related to local coordinate system 505 are filed in storage 512 and the data related to local coordinate system 508in storage 513.

DETAILED DESCRIPTION

(6) This invention in its different implementation options may be performed in the form of a technique (including technique implemented on a computer), a system, a device or machine-processable medium, containing instructions for implementation of the above technique.

(7) In this invention the device means a computer device, an electronic computer, a computer numeric control (CNC), a PLC (Programmable Logic Controller) and any other devices capable to perform specified, strictly defined sequence of operations (actions, instructions).

(8) The instruction unit means an electronic unit or an integrated circuit (micro-processor) which performs machine instructions.

(9) The instruction unit may read and perform machine instructions (programs) acquired from one or more data storage devices. The functions of data storage device may be implemented, but not limited to, hard disk drives (HDD), flash-drive, read-only memory, solid-state drive (SSD), optical drives.

(10) The programsequence of instructions designated for implementation of control of a computer or an instruction unit by the device.

(11) The stated mobile terminal indoor positioning technique and its implementation system may be performed in accordance with the below procedure.

(12) The mobile terminal hereinafter means any device capable to move together with the user and supply the information, especially, the information on its location. The example of the mobile terminal may be such devices, as smartphones, tablet computers, mobile phones, notebooks, netbooks, pedestrian and motor vehicle navigators, trackers and similar devices. They may include devices wearable on user's body, among which smart glass (Google Glass), smart watch (for example, Samsung Galaxy Gear), devices for sports and fitness and others may be mentioned.

(13) Regarding indoor positioning, it means positioning inside all facilities, man-made and natural, above-ground and underground, where positioning according to signals from navigation satellite is impractical due to significant signal attenuation inside building walls, ceilings and floorings.

(14) To solve the raised problem, a series of movements of at least one mobile terminal may be performed inside the building proposed for positioning, in their process inertial sensors of the mobile terminal may measure performances of terminal movement and non-inertial sensors of the mobile terminal may measure physical properties of indoor environment. For example, the list of mobile terminal sensors may include inertial sensors (for example, gyroscopes and accelerometers), magnetic field strength sensor (magnetometer), compass, pressure detector (barometer), image sensor (camera), microphone, touch screen, telecommunication and navigation modules, such as WiFi, BLE, NFC, 3G/LTE, GPS/GNSS and others. It is assumed that the mobile terminal is completed with at least a portion of specified sensors and modules, including inertial (for example, an accelerometer and a gyroscope) and non-inertial (for example, WiFi module, magnetometer and other) sensors. This assumption is not limiting one, as far as for example, smartphones and tablet computers generally have the complete package of said sensors.

(15) Outputs of the said sensors and modules may take recording in the process of terminal movements. Should it be inertial sensors, they may include accelerations and angular velocities measured with accelerometers and gyroscopes, as appropriate. Should it be non-inertial sensors, they may include, for example, Receiver Signal Strength Indicator (RSSI) for observed WiFi access points measured by WiFi module, three magnetic field induction vector projections measured by the magnetometer and other values. Measured values required for subsequent utilization may be provided with time stamps stored together with said output sensor values. For example, internal time of mobile terminal processor may be used as the time stamp.

(16) FIG. 1 shows the process of movement of mobile terminal 191 along continuous path 190 in indoor environment 192. The process of movement of one mobile terminal is shown by way of example only, though in general, more than one mobile terminal may move simultaneously indoors. At that description of positioning process may remain unchanged.

(17) For example, FIG. 1 shows 5 passages along 5 paths (110, 120, 130, 140 and 150), though the number of passages may differ. In the process of one of such passages, the mobile terminal was moved along path 110 across position 101 in direction 111 and subsequently, within the same path sectionacross position 103 in direction 112. In the process of another passage, the mobile terminal was moved along path 120, firstacross position 103 in direction 122, lateracross position 101 in direction 121. In the process of some more passages, the mobile terminal was moved across position 101 along path 130 in direction 131 and along path 140 in direction 141. These path sections do not contain passage across position 103. In the process of one more passage, the mobile terminal was moved across position 103 along path 150 in direction 151. Directions 111, 121, 131, 141, in which the mobile terminal was moved across position 101, as well as paths 130, 140 of mobile terminal movement are described in local coordinate system 102 associated with position 101. Directions 112, 122, 152, in which the mobile terminal was moved across position 103, as well as path 150 of mobile terminal movement, are described in local coordinate system 104 associated with position 103. Paths 110 and 120 are described in coordinate system 102 and in coordinate system 104.

(18) In the process of movement of the mobile terminal, patterns C.sub.k allowing for identification of mobile terminal location in a specific position P.sub.k with high degree of confidence, which coordinates in general may be unknown, may be identified with outputs of non-inertial sensors of the mobile terminal, where k may vary within 1 to N range. Hereafter, for convenience of statement only, the following expression, for example, is used: location of the mobile terminal in position P.sub.k. As far as position P.sub.k may be identified with define degree of confidence, the above expression, as well as similar expressions may mean that the mobile terminal was actually located within some vicinity to position P.sub.k, at that vicinity radius R may depend upon accuracy of detection of well identified position. Specific value R may vary for different applications. For example, for some geo-data services (or Location-Based Services, LBS), value R may make one meter or units of meters. For high accuracy positioning, value R may make units of decimeters or even centimeters. Subsequent description of the proposed technique is applicable to any positioning accuracy.

(19) Patterns allowing for identification of specific indoor location of the mobile terminal with high degree of confidence may have natural origin, but also may be generated artificially. Such patterns may be scalar, for example, the pressure measured by barometer, vector, for example, three-dimensional vector of magnetic field strength obtained from magnetometer output or matrix and even matrix sequences, for example an image or video obtained from camera output. In addition, the pattern may be a sequence of scalar, vector or matrix measurements over the specific time interval. For example, in some locations of indoor environment 192, significant variation of magnetic induction vector may be observed. At that, when mobile terminal 191 moves along path 190 running across definite positions, magnetic induction vector may trace out strictly defined curve different from similar curves obtained when the mobile terminal moves in other indoor environments or in the same location, but along the other path. The unique sequence of values of magnetic induction vector observed in this location may be considered as the signature, definitely showing the sequential passage of definite positions by the mobile terminal.

(20) The other signature example definitely certifying passage through the specific indoor environment area by the user or any other mobile object may be receipt of data unique sequence from small-radius beacon, for example tag NFC. One more example may be the signal of BLE-based radio-beacon, for example iBeacon. Location of the mobile terminal in a specific position may be identified with other positioning radio-beacons, if they are located indoors or outside.

(21) Recording of the specific location of the mobile terminal may be provided with decoding of the QR-code located indoors as a stamp and containing information on location or via identification of identity of the image of the specific indoor environment area. In both cases, images are obtained with mobile terminal camera. One more example of location recording may be loading of the position by the user via mobile terminal touch screen.

(22) Patterns used for detection of different well identified indoor positions may have different physical nature. For example, one well identified indoor position may be detected with unique vector of magnetic field strength, the other positionwith NFC signal or QR-code.

(23) Particular patterns may be detected with due account for techniques for detection of patterns known in the literature and stated, for example in Chapter 7, Book Pattern Recognition Principles, authors J. T. Tou, R. C. Gonzlez, Addison-Wesley, 1977.

(24) After definite time following start of mobile terminal (terminals) movement N of C.sub.1 . . . C.sub.N patterns may be identified, such patterns that occurrence of C.sub.k pattern in the data obtained from sensors may identify location of the mobile terminal in position P.sub.k with high degree of confidence, where k=1 . . . N, and N>=2. At that, position P.sub.k may be unknown in general.

(25) Let's continue to review the example of mobile terminal 191 movement in indoor environment 190. Every time, when the mobile terminal is in position 101 or 103, data specified by appropriate unique pattern are transmitted from mobile terminal sensors. This pattern does not appear when the mobile terminal is located in any other location. FIG. 1 shows by way of example only two well identified positions 101 and 103, though in general their quantity N may be more, i.e. N2. In the time moments when mobile terminal 191 was located in unknown, but well identified positions 101 and 103 with high degree of confidence, directions of its movement and sections of the path prior and following passage of these positions have been identified. These values are described in the local coordinate systems, 102 and 104, as appropriate.

(26) As far as the mobile terminal moves in indoor environment, measurements, acquired from mobile terminal sensors, may be checked with regard to availability of one of patterns C.sub.1 . . . C.sub.N. FIG. 2 shows this process, where path 220 of mobile terminal movement in indoor environment 221 is shown. Case N=2 is reviewed by way of example only, at that, review remains true for general case N>2.

(27) When position 207 is passed in time moment t.sub.1 (211), pattern C(t.sub.1) identified with 201 is transmitted from mobile terminal sensors. When position 208 is passed in time moment t.sub.2 (212), pattern C(t.sub.2) identified with 203 is transmitted from mobile terminal sensors. Let's suppose by way of example, that modified pattern C(t.sub.1) in time moment t.sub.1 has coincided with previously identified pattern C.sub.1, and modified pattern C(t.sub.2) in time moment t.sub.2 has coincided with previously identified pattern C.sub.2. Hence, we may make a conclusion, that mobile terminal position 207 in time moment t.sub.1 coincides with previously identified position P.sub.1 (202), and mobile terminal position 208 in time moment t.sub.2 coincides with previously identified position P.sub.2 (204).

(28) Further on, in accordance with data obtained from mobile terminal inertial sensors, the section of mobile terminal movement path 213 and 214 is computed within the time interval, including the moment of pattern detection, t.sub.1 and t.sub.2, as appropriate. In addition, the direction of 209 and 210 mobile terminal movement at the moment of pattern detection t.sub.1 and t.sub.2, as appropriate, may be detected. Listed computations may be performed in the local coordinate systems, 205 and 206, as appropriate, for positions P.sub.1 and P.sub.2. Later, computed path section as well as data obtained from non-inertial sensors at that time may be saved in the storage device.

(29) As an example of the local coordinate system, the cross-shaped X/Y axis may be reviewed hereafter, though review may be performed both for three-dimensional space and for any other system of coordinates, for example, for polar and spherical coordinate systems. The center of local coordinate system associated with well identified position may be located near the specified position, in particular case it may coincide with the specified position. Axes of the local coordinate system are directed in the known way. For example, direction of local coordinate system axe x may coincide with direction of magnetic induction vector in the center of the local coordinate system.

(30) The storage device may be located both on the mobile terminal itself and on remote server or cloud resource. The latter one is reasonable, should more than one mobile terminal move simultaneously in the indoor environment or participate in the described positioning process. In this case, communication between the mobile terminal and the remote storage is implemented via wireless communication line.

(31) Situations may occur when well identified positions are located close to each other. If this is a case, it is not required to associate each position P.sub.k with an individual local coordinate system. Such situation is shown in one of implementation option of the proposed technique, where the number of local coordinate systems may be less than N. In order to associate several positions with the local coordinate system shared by them, similarity of patterns may be identified and for similar patterns several positions P.sub.k are combined in the shared area. For example, all positions around a particular beacon, for example iBeacon, may be combined with a specific identifier into the shared area. Patterns for this examples may be signal strength levels of the above beacon measured in different positions, and criteria of similarity of patterns may consist in excess of some specified value by the signal strength level.

(32) Let's continue description of operation of the proposed system. For different pairs of patterns C.sub.i and C.sub.j, ij, parameters of shift of local coordinate systems of the mobile terminal in positions P.sub.i and P.sub.j corresponding to identified patterns C.sub.i and C.sub.j may be identified. At first, potential computation of mobile terminal movement path between time moments when sequential detection, at first, of the first pattern and later of the second one, is checked for selected pair of detected patterns. Later, if potential computation is verified, mobile terminal movement path may be computed over the specified time interval in relation to some reference point, and parameters for conversion of the second local coordinate system are defined in accordance with the obtained position in relation to the first local coordinate system.

(33) Let's suppose to be definite, that the mobile terminal is located in position P.sub.1 in time moment t.sub.1, and in position P.sub.2in time moment t.sub.2. The path between t.sub.1 and t.sub.2, as appropriate, may be computed on the basis of known Dead Reckoning (DR) algorithm in accordance with inertial data. This problem may be solved with more suitable for this problem type of algorithmPedestrian Dead Reckoning (PDR).

(34) By way of example let's review in short PDR-based computation of the path section for option N=2. Practicability of computation of the path between points P.sub.1 and P.sub.2 may be defined on the basis of difference t.sub.2t.sub.1, which may not exceed some allowable value associated with PDR computation error or any other DR implementation option. The simplest algorithm of successive PDR-based computation of the path may look like:
x.sub.t=x.sub.t-1+l.sub.t cos(.sub.t)(1)
y.sub.t=y.sub.t-1+l.sub.t sin(.sub.t)(2)

(35) where ttime which may be set integer without reduction of generality.

(36) x.sub.t and y.sub.tmobile terminal coordinates computed in the first local coordinate system (102),

(37) l.sub.ttrack length,

(38) .sub.tdirection (movement direction) in local coordinate system 102.

(39) Track length 4 may be computed on the basis of data obtained from accelerometers and movement direction .sub.t may be estimated on the basis of data obtained from a magnetic compass and gyroscope, as it is described, for example, in Section 10, BookPrinciples of GNSS Inertial and Multisensor Integrated Navigation Systems, by P. D. Groves, Artech House, 2008.

(40) Computation on the basis of (1) and (2) equations from time moment t=t.sub.1 through time moment t=t.sub.2 may allow for expression of coordinates of the local system 206 in coordinates of local system 205, i.e. to find parameters of system 206 displacement in relation to system 205. Let's identify coordinates of position P.sub.1 in coordinate system 205 with (x.sub.t1, y.sub.t1) and coordinates of position P.sub.2 in the same coordinate systemwith (x.sub.t2, y.sub.t2).

(41) FIG. 3 explains conversion of local coordinate systems. In the reviewed example, conversion of the local coordinate systems is set with parameters of displacement of the second coordinate origin in relation to the first one, a (320) and b (321), as appropriate and the angle of rotation of the second local system in relation to the first one (322).

(42) In general, displacement parameters may be expressed from known relations stated, for example, in Section 1.4, BookAnalytical Geometry, World Scientific Publishing by Vaisman I., 1997:
a=x.sub.t2x.sub.t2 cos()+y.sub.t2 sin(),(3)
a=y.sub.t2x.sub.t2 sin()+y.sub.t2 cos(),(4)

(43) where x.sub.t2 and y.sub.t2coordinates of point P.sub.2 in first coordinate system 310,

(44) x.sub.t2 and y.sub.t2coordinates of point P.sub.2 in second coordinate system 311,

(45) angle of rotation of the second local coordinate system in relation to the first one.

(46) It is convenient to align the center of the local coordinate system with appropriate well identified position. FIG. 3 shows the example appropriate to this case. Then point P.sub.2 may be in the center of the second coordinate system 311, where its coordinates are equal to zero or x.sub.t2=0, y.sub.t2=0. Due to this circumstance, parameters of displacement of the second coordinate system in relation to the first one are expressed without angle of rotation with the following simple way:
a=x.sub.t2,(5)
b=y.sub.t2,(6)

(47) Angle of rotation of the second local coordinate system in relation to the first one may be defined, for example, as the difference between measurements of mobile terminal magnetic compass obtained in time moments t.sub.2 and t.sub.1.

(48) Later, parameters of conversions (a, b, ) of the local coordinate systems of mobile terminal may be averaged for track set. Specified averaging may be implemented as follows. Computed parameters of shift of the second coordinate system in relation to the first one may be saved, previously saved displacement parameters for these coordinate systems may be found in the storage device, found and new displacement parameters may be averaged. Hence, coordinates , for positions P.sub.k, k=1 . . . N may be estimated. One implementation option provides for averaging of data acquired by different users with mobile terminals with different sensors. Due to this, errors of estimation of parameters for conversion of coordinates may be reduced. The other implementation option provides for identification of variety of parameters of mobile terminal sensors and with due account for identified statistics data obtained from the same sensors are averaged. These operations may be performed for each pair of detected patterns from C.sub.1 . . . C.sub.N set. As a result a set of N points, specified by unique patterns, coordinates of which in relation to each other are known with sufficient degree of confidence, may be identified.

(49) Further on, coordinates of positions P.sub.1, . . . , P.sub.N, estimated in the local coordinate systems may be recomputed into the global coordinate system. The global coordinate system may be, for example, geodetic coordinate system WGS 84. In some applications building coordinate system may be used as the global coordinate system. Let's review potential example of implementation of re-computation into one or other global coordination system.

(50) One of the implementation option is based upon assumption that one or several points from P.sub.1, P.sub.2, . . . , P.sub.N plurality have known global coordinates. For example, at least one of such points may be located outdoors, due to this global coordinates may be detected with GPS/GNSS receiver included into the mobile terminal. In the other example, global coordinates of one of such points may be detected via identification of the signal transmitted from small-radius radio-beacon, for example from NFC tag or BLE-based radio-beacon, for example iBeacon, provided these radio-beacons are installed indoors. Radio-beacon signal is received by appropriate module (NFC or BLE) of the mobile terminal. One more example is detection of global coordinates one of such points via decoding of QR-code located indoors and containing information on location. QR-code may be read with mobile terminal camera. Following the global coordinates are known at least in one point from P.sub.1, P.sub.2, . . . P.sub.N plural, it may not be difficult to compute global coordinates of remaining points via known relative coordinates.

(51) Due to the above operations saved data obtained from non-inertial sensors together with computed sections of mobile terminal movement paths are in the storage device. At that, as it was shown above, points P.sub.1, P.sub.2, . . . P.sub.N acquire global coordinates. Local coordinates of path sections running across the specified points are recomputed similarly into global coordinates. Hence, data obtained from non-inertial sensors in different points of movement paths are associated with global indoor coordinates.

(52) The example of data from non-inertial sensors may be magnetic field induction measured with mobile terminal magnetometer. Combination of magnetic field measurements in different indoor environment points is called magnetic map. The other example of data obtained from non-inertial sensors is signal strength (RSSI) from different WiFi access points measured by mobile terminal WiFi module. Combination of measurements of radio-signal strength for different WiFi access points in different points of indoor environment is called WiFi radio-map. Maps of other physical value measured with mobile terminal sensors may be prepared similarly to above examples. Maps acquired with the specified method may be used at subsequent stage of operation of described technique for mobile terminal positioning.

(53) Later, in the process of subsequent movement of the mobile terminal in the same indoor environment, mobile terminal position may be estimated on the basis of measurements of non-inertial sensors and previously prepared maps.

(54) Position estimation may be acquired, for example, with so-called Particle Filter. There is still no steady Russian equivalent for this term, hence, hereafter it is identified with PF (Particle Filter). PF theory is stated in the literature, for example, F. Gustafsson, Particle Filter Theory and Practice with Positioning Applications, IEEE A&E SYSTEMS MAGAZINE Vol. 25, No. 7, July 2010, Part 2, p. 53-81.

(55) PF algorithm essence is as follows. M objects called particles (there is still no steady Russian term, particles or samples may be used) are generated in random. Each particle may be reviewed as an object coordinate, i.e. as a pair of Cartesian coordinate in the reviewed example (x.sub.t.sup.i, y.sub.t.sup.i). Each particle is assigned with weight w.sub.t.sup.i, depending upon probability density value for this coordinate. Knowing the movement model, a new set of particles is generated via their movement into the new positions on the basis of inertial sensors. Hence, PF prediction stage is implemented. Subsequently, measurement (for example, Wi-Fi radio-field induction, magnetic field strength, map) is performed and particles weight is corrected (updated) on its basis.

(56) For the above reviewed example, the prediction stage consists of application of object (1) and (2) movement model to each particle, this results in a new set of particles:
x.sub.t.sup.i=x.sub.t-1.sup.i+l.sub.t.sup.i cos(.sub.t.sup.i),(7)
y.sub.t.sup.i=y.sub.t-1.sup.i+l.sub.t.sup.i cos(.sub.t.sup.i),(8) where inumber of particle, i=1, . . . , M,
l.sub.t.sup.iN(l.sub.t,.sub.lt.sup.2)
.sub.t.sup.iN(.sub.t,.sub..sup.2)(9) N(m,.sup.2)probability density function as per Gaussian law with mean m and dispersion .sup.2. lttrack length, .sub.tdirection (movement direction defined with PDR) .sub.lt.sup.2, .sub..sup.2,dispersion of track length and direction.

(57) New weight values are computed at the correction stage:
w.sub.t.sup.j=w.sub.t-1.sup.j.Math.p(z.sub.t|x.sub.t.sup.i,y.sub.t.sup.i), i=1, . . . ,M,(10) where p(z.sub.t|x.sub.t.sup.i,y.sub.t.sup.i)likelyhood function obtained on the basis of zt measurements, which may be WiFi signal strength, magnetic field strength and other output values of non-inertial sensors. Then normalization of weights is performed:

(58) $\begin{array}{cc}{w}_t^i=\frac{w_t^i}{\underset{i=1}{\overset{M}{.Math.}}{w}_t^i},i=1,\mathrm{.Math.},M& \left(11\right)\end{array}$ Current estimation of the position is generated, for example, as weighted mean for all particles:

(59) $\begin{array}{cc}{\hat{x}}_t=\underset{i=1}{\overset{M}{.Math.}}{x}_t^i{w}_t^j,{\hat{y}}_t=\underset{i=1}{\overset{M}{.Math.}}{y}_t^i{w}_t^j,& \left(12\right)\end{array}$

(60) One more PF specific featureresambling procedureknown from the literature, for example, from above publication, hence, it is not reviewed here.

(61) Mobile terminal position may be estimated as far as it moves farther in the same indoor environment on the basis of measurements of non-inertial sensors and previously prepared maps. For this purpose, the weights may be corrected in accordance with formula (10), where likelyhood function
p(z.sub.t|x.sub.t.sup.i,y.sub.t.sup.i)

(62) may have, for example the following pattern:

(63) $\begin{array}{cc}p\left(z_t|x_t^i,y_t^i\right)=\frac{1}{2{}_m^2}{e}^{-\frac{{\left(x_t^i-f_x\left(z_t\right)\right)}^2+{\left(y_t^i-f_y\left(z_t\right)\right)}^2}{2{}_m^2}}& \left(13\right)\end{array}$ where fx (zt), fy (zt)functions calling back estimations of mobile terminal coordinates on the basis of zt measurement and generated at the previous step of the map of distribution of output values of non-inertial sensors indoors, .sub.m.sup.2dispersion of the specified estimation.

(64) The pattern of fx (zt), fy (zt) functions depends upon the specific measurements used for positioning and used estimation method. For instance, for the option of utilization of signal strength measurement RSSI, some methods are known from the literature, for example, A. Kushki, K. Plataniotis, A. Venetsanopoulos, WLAN Positioning Systems, Cambridge University Press, 2012.

(65) One of proposed option implementation provide for additional updating of estimation of mobile terminal position, should one or several patterns C1, . . . , CN be identified in measurements. For example, should Ck pattern allowing for identification of the mobile terminal position in position Pk with high degree of confidence, be detected, position may be updated via reutilization of correction procedure (10). At that the likelyhood function may have, for example, the following pattern:

(66) $\begin{array}{cc}p\left(z_t|x_t^i,y_t^i\right)=\frac{1}{2{}_c^2}{e}^{-\frac{{\left(x_t^i-{\hat{x}}_k\right)}^2+{\left(y_t^i-{\hat{y}}_k\right)}^2}{2{}_c^2}}& \left(14\right)\end{array}$ where estimation of position Pk coordinates, previously obtained, .sub.c.sup.2dispersion of coordinate estimation.

(67) As far as estimation of coordinates , generated with large quantity of data, dispersion of estimation .sub.c.sup.2 may be not high, due to this re-correction (10) with likelyhood function (14) may allow for improvement of positioning accuracy (to reduce estimation dispersion).

(68) One of the proposed system implementation options assumes that the indoor environment layout is available (previously layout availability was not assumed). Should it be such a case, the following additional probability for re-computation of local coordinates of points P1, P2, . . . PN into global coordinates, in this case into coordinated of building layout, may appear. The layout imposes restrictions to location of points P1, P2, . . . PN, as well as to paths of mobile terminal movement between these points. Restrictions are caused due to availability of walls, crossings of which are prohibited. In additional, several areas of indoor environment, for example, individual rooms, may be inaccessible for visit. Alignment of paths and layout with due account for restrictions allows for locating points P1, P2, . . . PN and paths of mobile terminal movement between these points in such a way in order to meet restrictions imposed by the layout, i.e. prevent crossings of walls and visits to inaccessible locations. Following the points are located on the layout in such a way, coordinates of points P1, P2, . . . PN become known in layout coordinates. The operation related to alignment of paths and layout with due account for restrictions may be performed with different methods. Let's review, for instance, practicable implementation of alignment of paths and layout with PF.

(69) For example, location of points P1, P2, . . . PN and path of mobile terminal movement between these points meeting layout restrictions may be found as a result of sequence of actions described below. At that, let's suppose, to be definite, that displacements of all positions in relation to one of them, for example, positions P2, . . . PN in relation to P1, are known. Let's identify the prohibited layout area with W (from English wordwall). 1) To set the initial position of one point, for example, P1, in layout coordinates in allowed layout area. To re-compute coordinates of remaining points P2, . . . , PN into layout coordinates. 2) To use prediction equations (7) and (8) for compute mobile terminal movement path from point P1 to any other point, for example, P2. Correction equation (10) may be used, at that the likelyhood function may have the pattern:

(70) $\begin{array}{cc}p\left(z_t|x_t^i,y_t^i\right)=\{\begin{array}{cc}0,& \left(x_t^i,y_t^i\right)W\\ 1,& \left(x_t^i,y_t^i\right).Math.W\end{array}& \left(15\right)\end{array}$ The equation (15) takes into account layout restrictions by zeroing the weight of the particle which is trying to cross the wall. 3) To repeat item 2 for other pair of points Pi, Pj. 4) Should zeroing of all weights occur in the process of implementation of items 2 and 3, then to return to item 1 and to set a new initial position for point P1 in layout coordinates. 5) To repeat the preceding items, searching different initial positions of point P1 with an interval. As a result, only those initial positions may remain for which computed paths avoided crossing of walls. The best initial position may be selected, for example, according to the criteria of adjacency of paths estimated with equations (12) for two options: with due account for restriction (15) and without it.

(71) As a result, the initial position of one point in layout coordinates is detected, in relation to which coordinates of all remaining points on the layout become known.

(72) FIG. 4 shows the flow diagram for mobile terminal indoor positioning system, implementing the above positioning technique. The system operates as follows. A set of sensors 401 measures physical values in the process of mobile terminal indoor movement. Output values of sensors are transmitted to the computer 402 designated for generation of statistical models of sensor measurements in known positions and current measurement in the unknown position. Examples of statistical models of sensor measurements in known positions may be the radio-map described above or magnetic movement map. Should computer 402 successfully simulate the said statistical models, then probability estimation module 403 associated with it may estimate the probability of mobile terminal location in one of the known positions.

(73) As far as initial positions of the mobile terminal are not known in the process of movement, it is required to detect them in order the computer to be capable to implement its task. Pattern selection module 410, data storage 411 and coordinate converter 412 were added into the system for this purpose.

(74) Pattern selection module 410 is associated with a set of sensors 401. This module purpose is detection of such patterns C1 . . . CN in output data of sensors which ensure identification of location of the mobile terminal in position Pk with high degree of confidence, when pattern Ck appears in the data obtained from the sensors.

(75) Following identification of patterns, data obtained from sensors are saved in data storage 411, provided, that each storage is associated with one or several patterns Ck and positions Pk.

(76) FIG. 5 explains utilization of data storages in the proposed mobile terminal positioning system. Sections of mapped indoor environment are shown in the form of three areas: area 503 with local coordinate system 502 associated with well identified position 501, area 506 with local coordinate system 505 associated with well identified position 504 and area 509 with local coordinate system 508 associated with well identified position 507. Orientation of local coordinate systems 502, 505 and 508 may be definitely defined in the global coordinate system 510. Areas 503, 506 and 509 define approximate differentiation between local mapped indoor environment sections. At that, the data related to local coordinate system 502 are stored in the storage 511, data related to local coordinate system 505 are stored in storage 512 and data related to local coordinate system 508in storage 513.

(77) Data accumulated in the storage 411 (FIG. 4) are utilized in coordinate converter 412 for conversion of local coordinates and for estimation of global coordinates, as it is described above. As a result, coordinates of positions where output data of sensors are collected may be known. The specified coordinates combined with data are transmitted to computer 402 capable of generating the map of distribution of sensor output data indoors.

(78) Probability computation module 403 performs computations of probability of mobile terminal location in one of the known positions, for example, in line with equations (13) and (14). Data storage 411 and coordinate converter 412 may be located at the mobile terminal, but they also may be located at the remote server or cloud resource. The latter is reasonable, if more than one mobile terminal moves indoors or participates in the positioning process. Should it be the case, communication with pattern selection module 410 and computer 402 may be implemented via wireless communication line. Data storages may be implemented in the form of a portion of operating memory (RAMRandom Access Memory) of the mobile terminal or server depending upon where data storages are located. Mobile terminal processor, for example, of ARM Cortex-Ax type, may be used as a computer. Other computation modules may be implemented with a processor or hardware accelerator of the mobile terminal or the server.

(79) It is evident for this area specialist, that the specific options of implementation of this invention have been described here for the purpose of illustration, different modifications not beyond the scope and invention background scope are allowable.