A method for estimating position of a mobile device which includes receiving, from a network server, observed time difference of arrival (OTDOA) assistance data for a first plurality of cells from a base station almanac (BSA) accessible to the network server. The OTDOA assistance data is stored, within a memory of the mobile device, as a first micro-BSA. A position estimate for the mobile device is determined based upon time difference of arrival (TDOA) measurements associated with an initial subset of the first plurality of cells and initial OTDOA assistance data corresponding to the initial subset of the first plurality of cells. The initial OTDOA assistance data may be generated by the micro-BSA based upon an initial seed estimate.

According to one embodiment, a computer-implemented method for dynamic, cognitive hybrid positioning within an indoor environment includes: receiving fingerprinting training data corresponding to the indoor environment, trilateration data corresponding to the indoor environment, triangulation data corresponding to the indoor environment, or a combination of the fingerprinting training data, the trilateration data, and/or the triangulation data; estimating a layout of the indoor environment based at least in part on the fingerprinting training data; classifying at least some areas of the estimated layout according to one of a plurality of predetermined area types; and determining an optimum positioning technique to utilize for each area of the estimated layout, wherein the optimum positioning technique is determined based at least in part on the area type. Corresponding system and computer program product embodiments are also disclosed, as well as hybrid techniques for determining user position within an environment.

An apparatus comprising a transceiver, a processor and a memory. The transceiver may be configured to send/receive data messages to/from a plurality of vehicles. The processor may be configured to execute instructions. The memory may be configured to store instructions that, when executed, perform the steps of (A) generating signal distance calculations between the apparatus and at least three of the vehicles using the data messages, (B) calculating a plurality of potential positions of the vehicles using the signal distance calculations, (C) performing a scaling operation on the plurality potential positions of the vehicles to determine relative positions of the vehicles on a coordinate system, (D) implementing a procrusting procedure on the coordinate system to generate a corrected coordinate system and (F) determining changes of the relative positions using the corrected coordinate system.

Systems and methods for determining a location of one or more user equipment (UE) in a wireless system can comprise receiving reference signals via a location management unit having two or more co-located channels, wherein the two or more co-located channels are tightly synchronized with each other and utilizing the received reference signals to calculate a location of at least one UE among the one or more UE. Embodiments include multichannel synchronization with a standard deviation of less than or equal 10 ns. Embodiments can include two LMUs, with each LMU having internal synchronization, or one LMU with tightly synchronized signals.

The present disclosure describes a self-positioning system, a tracking beacon and a self-positioning method for a vehicle. The self-positioning system is configured to estimate an direction of arrival of a radio wave tracking beacon signal arriving at an antenna array of the vehicle from a non-stationary tracking beacon unit, estimate Euclidian distance between the self-positioning system and the tracking beacon unit by using wireless radio-frequency communication between the self-positioning system and the tracking beacon unit, and determine position data identifying a three-dimensional position of the self-positioning system with respect to tracking beacon unit on the basis of the estimates of the direction of arrival and the Euclidian distance.

An apparatus comprising a transceiver module and a processor. The transceiver may be configured to send/receive data messages to/from a plurality of vehicles. The processor may be configured to (i) determine a plurality of selected vehicles from the plurality of vehicles based on a selection criteria and (ii) calculate relative coordinates of the plurality of vehicles based on the data messages from the selected vehicles. The selection criteria may comprise determining (i) a target vehicle and (ii) at least two complementary vehicles. A predicted trajectory of the target vehicle may cross paths with a predicted trajectory of the apparatus. The complementary vehicles may be selected based on (i) an arrangement of the plurality of vehicles and (ii) speeds of the plurality of vehicles.

In one embodiment, a service obtains spatial information regarding a physical area. The service estimates locations of a device within the physical area over time, based on wireless signals sent by the device. The service generates a set of images based on the spatial information regarding the physical area and on the estimated locations of the device within the physical area over time. The service updates an estimated location of the device by inputting the generated set of images to a machine learning model trained to minimize a location estimation error.

A location server device includes a memory circuitry, a processor circuitry, and an interface. The location server device is configured to communicate, via the interface, with one or more positioning units configured to communicate with one or more electronic devices one or more first positioning signals at a first frequency. The processor circuitry is configured to select at least one of the one or more positioning units based on detecting a trigger event. The interface is configured to transmit, to the at least one selected positioning unit, an activation signal. The activation signal indicates to the at least one selected positioning unit to activate transmission of one or more second positioning signals at a second frequency that is different from the first frequency.

In one embodiment, a service obtains spatial information regarding a physical area. The service estimates locations of a device within the physical area over time, based on wireless signals sent by the device. The service generates a set of images based on the spatial information regarding the physical area and on the estimated locations of the device within the physical area over time. The service updates an estimated location of the device by inputting the generated set of images to a machine learning model trained to minimize a location estimation error.

Techniques for detecting and presenting rewards for presence are disclosed. Presence of a mobile device within a building is detected based on a plurality of triggers including a degradation of a first signal and a concurrent improvement of a second signal. The triggers are identified by a client-side application of the mobile device or a corresponding server-side processor with which the client-side application is in communication and from which the client-side application is configured to automatically receive available rewards. In response to detecting presence within the building, the server-side processor determines that a user of the mobile device is eligible for a reward, and the reward is provided to the user via the client-side application at the mobile device.