Techniques for determining a location of a client device using recursive phase vector subspace estimation are described. One technique includes receiving a plurality of angle-of-arrival (AoA) measurements from a plurality of access points (APs). Each AoA measurement includes a plurality of entries for phase values measured from a signal received from a client device at the plurality of APs. At least one AoA measurement of the plurality of AoA measurements that includes at least one of: (i) one or more entries with missing phase values and (ii) one or more entries with erroneous phase values is identified, based on a recursive phase estimation. The plurality of AoA measurements are updated based on the identified at least one AoA measurement. The location of the client device is determined, based on the updated plurality of AoA measurements.

There is provided mechanisms for determining direction of a second radio transceiver device with respect to a first radio transceiver device. The first radio transceiver device is configured to communicate with beams in a beam set. A method is performed by a processing unit. The method comprises obtaining a vector of radio parameter measurements from measurements performed on a radio link between the first radio transceiver device and the second radio transceiver device for one and the same location of the second radio transceiver device. The vector comprises a radio parameter measurement per each beam in the beam set. The method comprises determining the direction of the second radio transceiver device with respect to the first radio transceiver device by comparing the vector of radio parameter measurements to a set of candidate direction profiles.

Disclosed in the present invention is a method for estimating a direction of arrival of a sub-array partition type L-shaped coprime array based on fourth-order sampling covariance tensor denoising, which mainly solves problems of a damage to a signal structure and noise term interference to high-order virtual domain statistics in an existing method. The implementation steps are as follows: constructing an L-shaped coprime array partitioned with linear sub-arrays; modeling a receiving signal of the L-shaped coprime array and deriving a second-order cross-correlation matrix thereof, deriving a fourth-order covariance tensor based on the cross-correlation matrix; realizing fourth-order sampling covariance tensor denoising based on kernel tensor thresholding; deriving a fourth-order virtual domain signal based on denoised sampling covariance tensor; constructing a denoised structured virtual domain tensor; obtaining a direction of arrival estimation result by decomposing the structured virtual domain tensor.

Systems and methods are provided to simultaneously determine both angle of arrival (AoA) and angle of departure (AoD) of a signal transmitted between two or more radio frequency (RF)-enabled wireless devices (e.g., such as BLE modules). The disclosed systems and methods may be so implemented in one embodiment to determine AoD even in the case where the transmitting wireless device is at the same time operating in a departure (or AoD) transmitting mode by transmitting a RF signal from multiple antenna elements of at least one switched antenna array using a given switching pattern or sequence implemented by an array switch.

Techniques for determining a location of a client device using recursive phase vector subspace estimation are described. One technique includes receiving a plurality of angle-of-arrival (AoA) measurements from a plurality of access points (APs). Each AoA measurement includes a plurality of entries for phase values measured from a signal received from a client device at the plurality of APs. At least one AoA measurement of the plurality of AoA measurements that includes at least one of: (i) one or more entries with missing phase values and (ii) one or more entries with erroneous phase values is identified, based on a recursive phase estimation. The plurality of AoA measurements are updated based on the identified at least one AoA measurement. The location of the client device is determined, based on the updated plurality of AoA measurements.

Mechanisms compressive sampling to detect direction of arrival (DoA) of a signal of interest (SoI), comprising: in each of a plurality of receiver paths, receiving the SoI and producing a received signal using an antenna; and using a modulator to: receive a modulator input signal (MIS) based on the received signal produced by the antenna in the path; modulate the MIS at multiple points in time (MPIT) based on different ones of a plurality of pseudo-random numbers; and produce a plurality of modulated output signals in response to the modulating of the MIS at the MPIT; summing across the receiver paths the one of the modulated output signals produced by each of the receiver paths for each of the MPIT, to produce a plurality of sum signals each corresponding to one of the MPIT; and performing a compressed sensing recovery algorithm to recover the DoA of the SoI.

A radio receiver is made much more immune to jamming signals. A vector EM sensor, in a 2-dimensional (3-axis sensor) or 3-dimensional (6-axis sensor) sensor configuration, is combined with a unique digital rotation to a preferred direction to create a new reference channel and, using an advanced frequency domain noise mitigation algorithm or other noise cancellation algorithm, can effectively reject jamming and other interference signals and improve the signal-to-noise ratio (20-40 dB) and the receiving performance of the receiver. The method can cancel both near-field and far-field interference and improve accuracy for various applications concerned with establishing the direction, or bearing, to a source. A communication receiver with the vector sensor and the cancellation algorithm has unique anti-jamming capabilities even for multiple jamming sources.

Disclosed is a high-resolution accurate two-dimensional direction-of-arrival estimation method based on coarray tensor spatial spectrum searching with coprime planar array, which solves the problem of multi-dimensional signal loss and limited spatial spectrum resolution and accuracy in existing methods. The implementation steps are: constructing a coprime planar array; tensor signal modeling for the coprime planar array; deriving coarray statistics based on coprime planar array cross-correlation tensor; constructing the equivalent signals of a virtual uniform array; deriving a spatially smoothed fourth-order auto-correlation coarray tensor; realizing signal and noise subspace classification through coarray tensor feature extraction; performing high-resolution accurate two-dimensional direction-of-arrival estimation based on coarray tensor spatial spectrum searching. In the present method, multi-dimensional feature extraction based on coarray tensor statistics for coprime planar array is used to implement high-resolution, accurate two-dimensional direction-of-arrival estimation based on tensor spatial spectrum searching, and the method can be used for passive detection and target positioning.

Systems and methods are provided to simultaneously determine both angle of arrival (AoA) and angle of departure (AoD) of a signal transmitted between two or more radio frequency (RF)-enabled wireless devices (e.g., such as BLE modules). The disclosed systems and methods may be so implemented in one embodiment to determine AoD even in the case where the transmitting wireless device is at the same time operating in a departure (or AoD) transmitting mode by transmitting a RF signal from multiple antenna elements of at least one switched antenna array using a given switching pattern or sequence implemented by an array switch.

The present invention belongs to the field of array signal processing and relates to a composite tensor beamforming method for an electromagnetic vector coprime planar array. The method includes: building an electromagnetic vector coprime planar array; performing tensor modeling of an electromagnetic vector coprime planar array receiving signal; designing a three-dimensional weight tensor corresponding to a coprime sparse uniform sub-planar array; forming a tensor beam power pattern of the coprime sparse uniform sub-planar array; and performing electromagnetic vector coprime planar array tensor beamforming based on coprime composite processing of the sparse uniform sub-planar array. Starting from the principles of receiving signal tensor spatial filtering of two sparse uniform sub-planar arrays that compose the electromagnetic vector coprime planar array, the present invention forms a coprime composite processing method based on a sparse uniform sub-planar array output signal.