A UE includes: at least one sensor configured to provide at least one sensor measurement; and a processor configured to: determine first and second ranges between the UE and a positioning signal source based on first and second positioning signal measurements of first and second positioning signals from the positioning signal source corresponding to first and second times; determine whether a selected range of the first range or the second range is a multipath range based on the first range, the second range, and movement of the UE between the first time and the second time indicated by the at least one sensor measurement; and discount use of the selected range in a positioning technique in response to the selected range being determined to be a multipath range.

Disclosed are techniques for wireless communication. In an aspect, a UE obtains measurement data associated with at least one PRS. The UE transmits, to a BS in a first L1 or L2 PSI report opportunity, a first PSI report indicative of a first set of measurement values associated with the at least one PRS based on the measurement data. The UE further transmits, to the BS in a second L1 or L2 PSI report opportunity that is subsequent to the first L1 or L2 PSI report opportunity, a second PSI report that is indicative of a second set of measurement values associated with the at least one PRS based on the measurement data, the second set of measurement values being refined from the first set of measurement values.

Embodiments herein relate to a method performed by a first communication node (110; 121) for determining the position (of a second communication node (122) in a wireless communications network (100). The first communication node (110; 121) transmits a timing measurement message to the second communication node (122) as a beamformed transmission based on channel sounding feedback information received from the second communication node (122). The first communication node (110; 121) also receives an acknowledgement message from the second communication node (122) for the timing measurement message in the beamformed transmission. Furthermore, the first communication node (110; 121) determines the position of the second communication node (122) at least partly based on a transmission time of the timing measurement message and a reception time of the acknowledgement message. Embodiments of the first communication node (110; 121) are also described. Embodiments herein also relate to a second communication.

A network node for a wireless communication system is configured to localize a user node in a first localization operation carried out at a first frequency; determine an accuracy value associated with the first localization operation; and adjust at least one beam parameter for radio beams to be used in a second localization operation based on the determined accuracy value, the second localization operation carried out at a second frequency that is greater than the first frequency. The network node is configured to determine the accuracy value associated with the first localization operation by tracking a rate of change of an angle of a radio beacon signal transmitted from the user node relative to the network node.

A 5G-signal-based DOA fingerprint-based positioning method includes the following steps: dividing an initial area into a number of micro-cells, and estimating angle information of reference points in the divided micro-cells; storing the angle information of the reference point of each micro-cell and position information of the each micro-cell in a fingerprint database, and updating the angle information in the fingerprint database at regular intervals; wherein when there is a target in the initial area, estimating angle information of the target; matching the angle information of the target with the angle information in the fingerprint database to determine a micro-cell where the target is located to obtain position information of the target, so as to locate the target.

Systems and methods for determining user equipment (UE) locations within a wireless network using reference signals of the wireless network are described. The disclosed systems and methods utilize a plurality of in-phase and quadrature (I/Q) samples generated from signals provided by receive channels associated with two or more antennas of the wireless system. Based on received reference signal parameters the reference signal within the signals from each receive channel among the receive channels is identified. Based on the identified reference signal from each receive channel, an angle of arrival between a baseline of the two or more antennas and incident energy from the UE to the two or more antennas is determined. That angle of arrival is then used to calculate the location of the UE. The angle of arrival may be a horizontal angle of arrival and/or a vertical angle of arrival.

The described disclosure presents embodiments of an efficient shortwave radio technique using a network of multiple sites located in and around an operating region (e.g., continental USA), calibrated distributed beacons, a detailed knowledge of ionospheric behavior, and sophisticated operational tools and software, that geo-locates targets without depending on satellites. The embodiments of the technique described herein, for example, accurately may locate a target by utilizing remote field units, a network of radio receive sites, receivers that accept and time stamp pertinent signals, demodulators that recognize and extract meaningful data from received signals, communications from all receive sites to a Network Operations Center (NOC), communications from NOC to field units to keep shortwave channel choices relevant and effective, and a processor within the NOC that analyzes and evaluates data.

The disclosure relates to a detection method and a detection apparatus, the method including: calculating, when a location base station in an ultra-wideband location system receives a pulse response, values of a plurality of specified pulse response characteristics using the received pulse response, and using the calculated values as values of the plurality of specified pulse response characteristics of the location base station; calculating differences between the values of the plurality of specified pulse response characteristics of the location base station and values of the plurality of specified pulse response characteristics of the location base station at a previous time, and using the calculated differences as variations of the plurality of specified pulse response characteristics of the location base station; determining, based on at least the variations of the plurality of specified pulse response characteristics of the location base station and by means of a trained classifier, whether signal propagation in which the location base station participates is non-line-of-sight propagation.

In one embodiment, a service receives signal characteristic data indicative of characteristics of wireless signals received by one or more antennas located in a particular area. The service uses the received signal characteristic data as input to a Bayesian inference model to predict physical states of an object located in the particular area. A physical state of the object is indicative of at least one of: a mass, a velocity, an acceleration, a surface area, or a location of the object. The service updates the Bayesian inference model based in part on the predicted state of the object and a change in the received signal characteristic data and based in part by enforcing Newtonian motion dynamics on the predicted physical states.

Methods and systems for dynamically modifying a sampling operation of a sensor. The method includes obtaining a dynamically changing transmission characteristic based on an available channel bandwidth parameter. The dynamically changing transmission characteristic includes at least one of a sample rate, a time period, or a spectral bandwidth. The method further includes updating the sampling operation of the sensor based on the dynamically changing transmission characteristic. The method further includes measuring signal energy at a location of the sensor. The method further includes sampling the signal energy using the sampling operation to obtain sampled data. The method further includes providing the sampled data to a processing entity configured to analyze the data using a dynamically updated cross-ambiguity function.