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.

For example, a radar processor may be configured to determine a first 1D AoA spectrum corresponding to a first dimension of an Azimuth-Elevation domain based on radar Rx data, to determine a second 1D AoA spectrum corresponding to a second dimension of the Azimuth-Elevation domain based on the radar Rx data, to detect one or more first object hypotheses in the first dimension based on the first 1D AoA spectrum, to detect one or more second object hypotheses in the second dimension based on the second 1D AoA spectrum, to determine a plurality of 2D object hypotheses corresponding to the Azimuth-Elevation domain based on the first object hypotheses and the second object hypotheses, and to generate 2D AoA information based on a 2D AoA spectrum analysis of the radar Rx data according to the plurality of 2D object hypotheses.

For example, a radar processor may be configured to determine a first 1D AoA spectrum corresponding to a first dimension of an Azimuth-Elevation domain based on radar Rx data, to determine a second 1D AoA spectrum corresponding to a second dimension of the Azimuth-Elevation domain based on the radar Rx data, to detect one or more first object hypotheses in the first dimension based on the first 1D AoA spectrum, to detect one or more second object hypotheses in the second dimension based on the second 1D AoA spectrum, to determine a plurality of 2D object hypotheses corresponding to the Azimuth-Elevation domain based on the first object hypotheses and the second object hypotheses, and to generate 2D AoA information based on a 2D AoA spectrum analysis of the radar Rx data according to the plurality of 2D object hypotheses.

Disclosed is a method comprising obtaining information comprising a location of a terminal device, obtaining an angle of arrival (342) of a signal transmitted by the terminal device, determining an expected angle of arrival (344) based, at least partly, on the location of the terminal device, determining if the angle of arrival of the signal transmitted by the terminal device and the expected angle of arrival correspond to each other, and if they do not performing an action associated with an incorrect reported location.

A radio station, in particular an access point, for client localization in a multipath indoor environment is disclosed. The radio station comprises: a circular antenna array comprising uniform circularly arranged antenna elements; and a processor configured to: transform first input data from the circular antenna array into second input data using a transform that transforms the first steering vector of the circular antenna array into a second steering vector of a virtual linear antenna array; and transform the second input data into third input data by using a transform that transforms the second steering vector of the virtual linear antenna array into a third steering vector of a second virtual linear antenna array, comprising a larger number of antenna elements than the virtual linear antenna array; and determine an angle of arrival based on the third input data.

A radio station, in particular an access point, for client localization in a multipath indoor environment is disclosed. The radio station comprises: a circular antenna array comprising uniform circularly arranged antenna elements; and a processor configured to: transform first input data from the circular antenna array into second input data using a transform that transforms the first steering vector of the circular antenna array into a second steering vector of a virtual linear antenna array; and transform the second input data into third input data by using a transform that transforms the second steering vector of the virtual linear antenna array into a third steering vector of a second virtual linear antenna array, comprising a larger number of antenna elements than the virtual linear antenna array; and determine an angle of arrival based on the third input data.

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.

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 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.