Facilitation of determination of detailed location of a source signal is provided. In one embodiment, a device comprises a memory that stores computer executable components; and a processor that executes computer executable components stored in the memory. The computer executable components can comprise: a low-resolution computation logic component that implements a coarse algorithm and determines an approximate direction of arrival (DOA) of a source signal of an input signal, wherein the coarse algorithm uses both a coarse spatial grid and input data received from the input signal to determine the approximate DOA; and an error estimation logic component that estimates an estimation error of the coarse algorithm, and wherein the error estimation logic component uses the estimation error and the approximate DOA to determine a spatial interval range.

A corner sensor for a motor vehicle capable of communicating over a wireless communication link with an authentication device and including: an array of antennae; and an electronic control unit configured to: determine the phase difference between the segments received by the antennae; determine the probability of each angle of incidence over a range of angles that is predetermined on the basis of the determined phase differences and of a predefined table, so as to determine a probability profile; measure the power of the segments received by the antennae of the array of antennae so as to determine a power profile; determine the actual angle of incidence of the signal by multiplying the probability profile with the power profile; and send the value of the determined actual angle of incidence to a central electronic control unit of the vehicle.

A corner sensor for a motor vehicle capable of communicating over a wireless communication link with an authentication device and including: an array of antennae; and an electronic control unit configured to: determine the phase difference between the segments received by the antennae; determine the probability of each angle of incidence over a range of angles that is predetermined on the basis of the determined phase differences and of a predefined table, so as to determine a probability profile; measure the power of the segments received by the antennae of the array of antennae so as to determine a power profile; determine the actual angle of incidence of the signal by multiplying the probability profile with the power profile; and send the value of the determined actual angle of incidence to a central electronic control unit of the vehicle.

Disclosed is a method and apparatus for estimating a signal source, the method including receiving, in at least one receiving node, a signal emitted from a signal source, extracting a steering matrix corresponding to a virtual antenna array by vectorizing the signal received in the at least one receiving node, and estimating a location of the signal source based on the steering matrix.

Embodiments herein describe performing AoA resolving to identify a plurality of AoAs corresponding to a multipath signal and then using AP voting to identify a location of the client device. AoA resolving enables an AP to identify the different angles at which a multipath signal reaches the AP. That is, due to reflections, a wireless signal transmitted by a single client device may reach the AP using multiple paths that each has their own AoA. The AP can perform AoA resolving to identify the AoAs for the different paths in a multipath signal. In one embodiment, the AoAs for two APs (or a subset of the APs) can be used to identify cross points or intersection points that represent candidate locations of the client device. A voting module can determine whether those cross points corresponds to AoAs identified by the remaining APs.

Aspects of the present invention provide systems and methods for distributed signal processing of indoor localization signals wherein statistical algorithms and machine learning are used in place of a fingerprint map. The disclosure relates to calculation of angle and distance based on measurements of an indoor localization signal, followed by energy-efficient distribution of signal processing. Local signal processing is performed using any of multiple eigen structure algorithms or a linear probabilistic inference, before cloud-based signal processing is performed using a nonlinear probabilistic inference and machine learning that's been trained with historical data transmitted by the base stations and time-of-day location patterns. Without having to generate and constantly update an energy-exorbitant fingerprint map, the disclosed system reduces localization error to merely 50 cm with 95% probability without compromising energy-efficiency to rival the accuracy of indoor localization systems that utilize fingerprinting.

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

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

Approaches are described for antenna configuration in an antenna array of limited array size and channel state information (CSI) collection and analysis, to improve accuracy of angle of arrival (AoA) estimations for localizing a client device's position. Signals can be received from groups of antennas and CSI data can be generated from the signals. The CSI data can be combined, where the combined CSI data represents CSI data measurements of multiple signals received from a plurality of antenna subsets, without requiring physical installation of additional antennas to the limited antenna array to make the CSI data measurements. The angle of arrival (AoA) of the signals is estimated based on the combined CSI, and the AoA estimation can be used to determine the client device's location, and for other location services, such as identifying a person's location, tracking and managing inventory of objects, commute prediction, and the like.

A subarray spatial averaging unit performs spatial averaging of correlation matrices by dividing a received signal of an array antenna to a plurality of subarrays having different shapes, and calculating these correlation matrices for the respective subarrays having different shapes. An eigenvalue expanding unit performs eigenvalue expansion of correlation matrices for the respective plurality of subarrays having different shapes after spatial averaging. A wave count estimating unit estimates an incoming wave count by integrating eigenvalues of the plurality of subarrays having different shapes obtained by the eigenvalue expanding unit.