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.

Determination is made as to whether or not to enable communications by a communication processing unit with an on-board unit, on the basis of a receive strength detected from a radio wave received via a communication antenna, and the angle of arrival and the receive strength of a direct wave and the angle of arrival and the receive strength of a reflected wave, the angles of arrival and the receive strengths being estimated from one or more radio waves received via an angle measurement antenna.

System, computer products, and methods can improve the resolution of data from a sensor array. One of these methods include receiving, from an analog to digital converter, a series of measurements representing frequency samples and spatial samples from a sensor array. The method includes generating a plurality of factors based on a polynomial. The method includes applying one or more complex weights to the measurements based on the factors. The method includes combining the complex weighted measurements into a plurality of values. The method also includes identifying a characteristic of an object detected by the sensor array based on the plurality of values.

System, computer products, and methods can improve the resolution of data from a sensor array. One of these methods include receiving, from an analog to digital converter, a series of measurements representing frequency samples and spatial samples from a sensor array. The method includes generating a plurality of factors based on a polynomial. The method includes applying one or more complex weights to the measurements based on the factors. The method includes combining the complex weighted measurements into a plurality of values. The method also includes identifying a characteristic of an object detected by the sensor array based on the plurality of values.

Disclosed is a spatial spectrum estimation method with enhanced degree-of-freedom based on block sampling tensor construction for coprime planar array, which mainly solves the multi-dimensional information loss in signals and degree-of-freedom limitation in the existing methods and which is implemented by the following steps: constructing a coprime planar array; modeling block sampling tensors of the coprime planar array; deducing coarray statistics based on the block sampling cross-correlation tensor; obtaining block sampling coarray signals of a virtual uniform array; constructing a three-dimensional block sampling coarray tensor and its fourth-order auto-correlation statistics; constructing signal and noise subspaces based on fourth-order auto-correlation tensor decomposition; estimating a tensor spatial spectrum with enhanced degrees-of-freedom. In the present disclosure, the block sampling tensors of the coprime planar array is constructed, where a coarray tensor is deduced, to realize tensor spatial spectrum estimation with enhanced degrees-of-freedom by extracting signal-to-signal subspace features from the four-order self-correlation tensor.

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.

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.

The invention relates to a method and a device for estimating an angle of arrival of an incident radio signal in relation to a predetermined reference direction by using a set of N receiving paths comprising at least one directional antenna pointing in N different receiving directions, wherein only one sub-set of at least two receiving paths with adjacent antenna directions in said set of antennas delivers a measured power at reception. The device comprises modules suitable for: determining a number of receiving paths delivering a measured power forming said sub-set, and a reference index corresponding to a first receiving path in a direction in which extends the set of antenna directions of said sub-set; selecting the measured powers and obtaining a value to attribute to the non-measured powers to form a completed power signal; by applying a discrete Fourier transform (DFT) to said completed power signal, calculating at least one transformed value among the transformed values corresponding to a first, second, and third frequency line of the DFT; and, using the transformed value(s), applying an estimator of the angle of arrival, depending on the reference index.

The invention relates to a method and a device for estimating an angle of arrival of an incident radio signal in relation to a predetermined reference direction by using a set of N receiving paths comprising at least one directional antenna pointing in N different receiving directions, wherein only one sub-set of at least two receiving paths with adjacent antenna directions in said set of antennas delivers a measured power at reception. The device comprises modules suitable for: determining a number of receiving paths delivering a measured power forming said sub-set, and a reference index corresponding to a first receiving path in a direction in which extends the set of antenna directions of said sub-set; selecting the measured powers and obtaining a value to attribute to the non-measured powers to form a completed power signal; by applying a discrete Fourier transform (DFT) to said completed power signal, calculating at least one transformed value among the transformed values corresponding to a first, second, and third frequency line of the DFT; and, using the transformed value(s), applying an estimator of the angle of arrival, depending on the reference index.

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.