A method and system of infrastructure re-purposed RSS signatures for mobile device indoor navigation and positioning. The method comprises determining a common spatial region in accordance with a first spatial region partially superimposed upon an at least a second spatial region, the first and at least a second spatial regions associated with a first and an at least a second wireless communication received signal strength (RSS) signatures respectively; and generating, based at least in part on the first and at least a second RSS signatures, a re-purposed RSS signature associated with the common spatial region.

An autonomous vehicle (AV) has an electromagnetic (EM) sensor that captures EM sensor data about the environment. The captured EM data is processed to generate an EM map of the environment. The EM data may also be used to identify EM characteristics of the environment. The EM map may be used to modify control and operation of the AV and may also be used to signal conditions to another system managing AVs, to correct localization of the AV, identify anomalies in the environment, and modify a previous signature EM map.

An edge device includes a first antenna array and a control circuitry that senses a surrounding of the edge device to recognize a user equipment (UE) in motion as valid to receive services from the edge device. A position of the UE in motion from the edge device is tracked to execute beamforming for directing a first beam of radio frequency (RF) signal having a signal strength greater than a threshold to the UE in motion. A first portion of the first antenna array is used to sense the surrounding area and one or more second portions of the first antenna array is used for the beamforming to direct the first beam of RF signal in a defined radiation pattern. Further, the defined radiation pattern of the first beam of RF signal is dynamically updated based on a change in a position of the UE in motion.

According to the present invention, a radio map construction method uses a genetic algorithm and comprises the steps of: (a) generating a plurality of chromosomes, each including a set of pairs consisting of a fingerprint labeled with an address and a position selected within a region s of the address; (b) generating a temporary radio map by using the pairs of the chromosomes; (c) arranging collected fingerprint sequences by using the temporary radio map; and (d) evaluating the placement of the fingerprint sequences.

In one embodiment, a technique is provided for wireless position determination wherein identification information and positional information about wireless beacons are supplied from a central database maintained by a central facility to a wireless computing device, the identification information and positional information usable by a wireless positioning system of the wireless computing device to estimate a position of the wireless computing device. The central facility receives a contribution from the wireless computing device of identification information and positional information for at least one wireless beacon, the positional information of the at least one wireless beacon estimated based on the position of the wireless computing device. It updates the central database based on the identification information and positional information for the at least one wireless beacon and provides, from the central database, updated identification information and positional information about wireless beacons to at least one other wireless computing device.

Device-free localization for smart indoor environments within an indoor area covered by wireless networks is detected using active off-the-shelf-devices would be beneficial in a wide range of applications. By exploiting existing wireless communication signals and machine learning techniques in order to automatically detect entrance into the area, and track the location of a moving subject within the sensing area a low cost robust long-term tracking system can be established. A machine learning component is established to minimize the need for user annotation and overcome temporal instabilities via a semi-supervised framework. After establishing a robust base learner mapping wireless signals to different physical locations from a small amount of labeled data; during its lifetime, the learner automatically re-trains when the uncertainty level rises significantly. Additionally, an automatic change-point detection process is employed setting a query for updating the outdated model and the decision boundaries.

Methods and apparatus for assigning a role to a new lighting node in a lighting system based on a received signal strength indicator (RSSI) fingerprint of the new lighting node. For example, methods and apparatus described herein are directed to determining RSSI fingerprints for each lighting node in a lighting system, matching one of the RSSI fingerprints to the RSSI fingerprint of a new lighting node, and assigning a role to the new 5 lighting node. The assigned role is the same role as previously assigned to the matching lighting node.

A method is provided that includes obtaining one or more pieces of crowd-sourced information. A respective crowd-sourced information is at least indicative of a location at which the respective crowd-sourced information was gathered. The method determines a set of tiles at least partially based on the one or more geographical areas. For at least one tile, the method obtains one or more pieces of fingerprint information comprised by the one or more pieces of crowd-sourced information that were gathered within the respective area of the respective tile and determines an area type and/or context information indicative of a type of venue located within the respective tile and/or context the respective area of the respective tile is used for. The area type and/or context information is determined at least partially based on the one or more pieces of fingerprint information. A corresponding apparatus and computer program product are also provided.

A technology for improving a positioning precision is desired in a case where a positioning precision of a positioning signal is relatively low. A location estimation apparatus includes a sub area extraction section configured to extract one or more sub areas including at least a part of a region defined by a location and an error range indicated by positioning information, a precision parameter extraction section configured to refer to map information in which area identification information is associated with a precision parameter indicating a positioning precision and extract, with regard to each of the one or more sub areas extracted by the sub area extraction section, the precision parameter, and an output section configured to output the sub area where the positioning precision indicated by the precision parameter is equal to or worse than the positioning precision indicated by the positioning information, as a location of a moving object.

Results of measurements by a mobile device on radio signals transmitted by at least one transmitter are obtained. The results of measurements comprise characteristics of the radio signals at each of a plurality of locations of measurements at a particular site, and indications of the locations of measurement. The results of measurement and the indications of the locations are provided and used as a basis for a generation of a radio map for use in supporting a positioning of mobile devices at the site. In addition, a user input to the mobile device is detected, the user input defining a localization area at the site that is to be covered by the radio map. A representation of the defined localization area is provided in addition for use in connection with the radio map.