A transmission source position estimation system includes a sensor and a position estimation apparatus. The sensor is provided with a characteristic vector classification part and an attribute data extraction part. The characteristic vector classification part classifies a set of characteristic vectors obtained from received signal data of a transmitted wave, into subsets in a feature space. The attribute data extraction part extracts attribute data for each of the subsets and outputs the extracted data as an attribute data sequence. The position estimation apparatus is provided with a data combination part and a position estimation part. The data combination part combines attribute data that match or are similar to at least one attribute for a plurality of attribute data sequences. The position estimation part estimates the position of the transmission source from the combined attribute data and the position of the sensor that receives the transmitted wave.

A method of determining the position of a moving unmanned aerial vehicle (UAV) is provided. The method comprises obtaining, for each of three or more base stations, one or more measurements of a carrier frequency offset (CFO) for one or more signals sent between a moving UAV and the respective base station. The method further comprises inputting the CFO measurements into a model to determine a position of the UAV, in which inputs to the model do not include range measurements of the UAV with respect to the three or more base stations.

Embodiments include methods for determining a movement state of a user equipment (UE) operating in a radio access network (RAN). Such methods include performing positioning measurements on signals received from a plurality of transmission points (TPs) in the RAN, including first measurements of Doppler shift of signals from a first TP, second measurements of Doppler shift of signals from a second TP that is spatially separated from the first TP, and third measurements of signals from a third TP. The third TP can be the same as the first or second TP, or spatially separated from both. Such methods include determining a UE movement state based on the positioning measurements and an interacting multiple-model (IMM) that includes a first almost-constant velocity model, a second maneuver velocity model, and a Doppler shift bias state common to the first and second models. Other embodiments include complementary methods for a RAN node.

Systems, device configurations, and processes are provided for blind opportunistic navigation (BON) including cognitive deciphering of partially known signals of opportunity and blind Doppler estimation from LEO satellite signal. In one embodiment a method includes receiving a signal of opportunity and using a framework for BON. In one embodiment, the framework includes performing blind Doppler estimation and tracking, performing coherent integration, and performing blind beacon detection/tracking. Coherent integration may be performed once a blind estimate of the Doppler is produced, and detecting symbols of a beacon sequence is performed for at least one of acquiring, tracking, and navigating with the received signal of opportunity. According to another embodiment, a method for blind Doppler estimation, includes receiving a signal of opportunity, performing an initial wipe-off operation, performing a blind residual Doppler estimation, and performing a Doppler ambiguity resolution.

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.

In a positioning system, a plurality of transmitter units (2, 3, 4, 5) transmit respective transmitter-specific identification signals at intervals, which are received at a mobile receiver unit (7). A processing system (7; 9) identifies the transmitter unit that transmitted each received identification signal, and, for each signal, determines range data from time of arrival data and determines distance data from Doppler shift information. The range data and distance data are compared to determine range error data. A position estimate for the mobile receiver unit (7) is determined by solving an optimisation problem using range estimates determined for the plurality of transmitter units, weighted in dependence on the range error data.

An enhanced L-band Digital Aeronautical Communications System (LDACS) includes a plurality of LDACS ground stations, each transmitting a respective carrier signal. An LDACS airborne station may be configured to communicate with the plurality of LDACS ground stations and determine position information based upon respective Doppler shifts in the plurality of carrier signals.

A method by a first network node for handling directions of transmission of beamformed beams by a first radio network node. Both nodes operate in a wireless communications network. The first network node determines, out of a set of directions in which the first radio network node is capable of transmitting the beams, a subset of directions of transmission of the beams having a probability of detection above a threshold, by a first wireless device. The determining is based on data obtained from previous attempts of positioning one or more second wireless devices using at least some of the directions. The first network node also initiates providing, to at least one of the first radio network node and a second network node operating in the wireless communications network, an indication of the determined subset.

This application relates to the field of wireless communications and self-driving/intelligent driving, and in particular, to the field of collaborative radars. In a solution of this application, a first apparatus receives first information from a second apparatus; the first apparatus determines, based on the first information, priorities of a plurality of time-frequency resources included in a first time-domain range; and the first apparatus determines a first time-frequency resource in the plurality of time-frequency resources, where a priority of the first time-frequency resource is not lower than a priority of a time-frequency resource other than the first time-frequency resource in the plurality of time-frequency resources. A time-frequency resource with a comparatively high priority is selected to send a radar signal, to reduce a probability of a resource collision, and reduce or avoid interference between radars, especially collaborative radars.

A method includes obtaining multiple geolocation measurements, where each geolocation measurement is generated using cross-ambiguity function (CAF) detection. The geolocation measurements are associated with at least one signal from at least one signal source and received by multiple receivers. The method also includes associating related geolocation measurements to form at least one collection of related geolocation measurements, where each collection of related geolocation measurements is associated with a common one of the at least one signal received by at least some of the receivers. The method further includes performing geolocation using the at least one collection of related geolocation measurements to identify one or more geolocations of the at least one signal source.