The present invention relates to a geolocation system and method for a multi-path environment. The geolocation system comprises one or more emitters (201a . . . 201n), one or more sensors (202a . . . 202n) comprising at least one processor. A first processor (204) estimates angle of arrival (AOA) and time of arrival (TOA) from the signals received from said one or more emitters (201a . . . 201n). A second processor (205) determines clusters based on the (AOA) and (TOA) data. The system also comprises a central node (207) in communication with at least one sensor (202a . . . 202n) and configured to estimate geolocation of one or more emitters (201a . . . 201n) wherein, said second processor (205) clusters data for the one or more emitters (201a . . . 201n) by executing a non-parametric Bayesian technique and said central node (207) utilizes hybrid angle of arrival-time difference of arrival (AOA-TDOA) technique to determine geolocation of each of the emitters (201a . . . 201n).

There is provided a method comprising receiving at least one measured signal characteristic from a user equipment, the user equipment being located at a user equipment location; comparing the at least one measured signal characteristic to at least one of a plurality of signal characteristics, each signal characteristic being associated with a respective measurement point; and determining, based on the comparing, a probability that the user equipment location is a first location.

A target device positioning method and a mobile terminal are provided. The mobile terminal determines, according to obtained measurement signals sent by a target device from a trigger moment to a current measurement moment, azimuths of the target device relative to the mobile terminal at the moments, obtains an original motion trail of the target device from the trigger moment to the current measurement moment, determines an effective motion trail of the target device according to the original motion trail and change rates of the azimuths of the target device relative to the mobile terminal at the moments, performs matching with a map according to an azimuth of the target device relative to the mobile terminal at the current measurement moment and the effective motion trail of the target device, to determine location information of the target device, and displays the location information of the target device.

A system and method for determination the relative location of a mobile object is described that includes building a database of known/expected locations with the exact longitude and latitude for each location. Next, an estimated location for a mobile object is generated using information from the cellular network and an area boundary is defined around the mobile object that defines, with some probability, where the object is actually located. The known locations in the database that fall within the area boundary are then identified and a relative probability is calculated for each known location that indicates its relative likelihood of where the mobile object is actually located. From this information at least the most probable location of the mobile object is determined along with a measure of estimation confidence.

According to an embodiment, a communication apparatus acquires tag data of each wireless tag at a plurality of positions of a moved antenna via an antenna and inputs the tag data into a learned model. The communication apparatus acquires, on the basis of the input of the tag data of each wireless tag into the learned model, data indicating a level regarding the range in which each wireless tag is present from the learned model. In addition, the communication apparatus controls, in a case where the levels of the plurality of wireless tags acquired on the basis of one or more measurement processes for a plurality of wireless tags do not satisfy a condition, a measurement process involving a change of a measurement aspect with respect to the plurality of wireless tags.

Techniques are provided for determining a position of a mobile device. An example method of reporting a probability distribution for positioning a mobile device includes obtaining positioning measurements, determining one or more probability distributions of one or more positioning metrics based on the positioning measurements, determining a parametric representation of the one or more probability distributions, and reporting the parametric representation.

A technique for detecting the occurrence of an event, and for estimating other event-related information, by analyzing the barometric pressure in the vicinity of one or more wireless terminals. The disclosed detection technique is based on the recognition that the barometric sensor on various wireless terminals, such as smartphones, is capable of measuring very subtle changes in the atmospheric pressure. The disclosed detection technique is also based on the additional recognition of how some of the changes in the atmospheric pressure, as measured by a wireless terminal, correlate to various events that occur within a building or other defined area. For example, the disclosed technique can detect an entry door opening or closing by analyzing a resultant pressure wave having a particular transient that is perceptible by one or more wireless terminals in the area and analyzed by a detection engine.

A system and method are provided for monitoring, collecting and aggregating position information from multiple independent data sources to localize a position of an aircraft operating worldwide. The localized position information is provided to one or more end-users or stakeholders in a format for direct integration into one or more mapping and/or situational awareness display applications. Information is collected from a plurality of monitored data sources. Weighted values are applied to certain of the information collected from the plurality of data sources according to known or predictable/determinable static and/or dynamic accuracy errors and latencies of the information provided. A detailed analytic algorithm is applied to provide a probabilistic analysis that results in a resolution of a real-time, or near real-time, aircraft location, as well as an ability to accurately predict an aircraft location along a track at some future time.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a first wireless communication device may determine, based at least in part on a first model, an estimated position of the first wireless communication device. The first wireless communication device may determine, based at least in part on a second model, an estimated direction for transmission of a packet to a second wireless communication device. The first wireless communication device may determine, based at least in part on a third model, an estimated transmit power for transmission of the packet. The first wireless communication device may determine, using a neural network, a narrow beam based at least in part on the estimated position, the estimated direction, and the estimated transmit power. The first wireless communication device may transmit the packet on the narrow beam to the second wireless communication device. Numerous other aspects are provided.

In an aspect, a UE obtains information (e.g., UE-specific information) associated with a set of triggering criteria (e.g., from a server, a serving network, e.g., in conjunction with or separate from a set of neural network functions) for a set of neural network functions, the set of neural network functions configured to facilitate positioning measurement feature processing at the UE, the set of neural network functions being generated dynamically based on machine-learning associated with one or more historical measurement procedures. The UE obtains positioning measurement data associated with a location of the UE, and processes the positioning measurement data into a respective set of positioning measurement features based at least in part upon the positioning measurement data and at least one neural network function from the set of neural network functions that is triggered by at least one triggering criterion from the set of triggering criteria. The UE reports the processed set of positioning measurement features to a network component (e.g., BS, LMF, etc.).