Systems and methods are provided in which a direction of arrival of a radio frequency (RF) signal received by a plurality of antennas is determined. A plurality of first converter receives RF signals from the plurality of antennas and outputs a minimum of a first optical signal and a second optical signal each modulated by their corresponding RF signal. A plurality of second converters receives a minimum of the first optical signal via a first optical channel that introduces a first delay and the second optical signal via a second optical channel that introduces a second delay. The second converter outputs a first RF signal that corresponds to the RF modulation on the first optical signal and a second RF signal that corresponds to the RF modulation on the second optical signal. A switch serially receives, from the second converter outputs, the first RF signal and the second RF signal. A direction finding subsystem determines a direction of arrival using a phase difference between the first RF signal and the second RF signal.

Systems and methods are provided in which a direction of arrival of a radio frequency (RF) signal received by a plurality of antennas is determined. A plurality of first converter receives RF signals from the plurality of antennas and outputs a minimum of a first optical signal and a second optical signal each modulated by their corresponding RF signal. A plurality of second converters receives a minimum of the first optical signal via a first optical channel that introduces a first delay and the second optical signal via a second optical channel that introduces a second delay. The second converter outputs a first RF signal that corresponds to the RF modulation on the first optical signal and a second RF signal that corresponds to the RF modulation on the second optical signal. A switch serially receives, from the second converter outputs, the first RF signal and the second RF signal. A direction finding subsystem determines a direction of arrival using a phase difference between the first RF signal and the second RF signal.

A two-dimensional direction-of-arrival estimation method for a coprime planar array based on structured coarray tensor processing, the method includes: deploying a coprime planar array; modeling a tensor of the received signals; deriving the second-order equivalent signals of an augmented virtual array based on cross-correlation tensor transformation; deploying a three-dimensional coarray tensor of the virtual array; deploying a five-dimensional coarray tensor based on a coarray tensor dimension extension strategy; forming a structured coarray tensor including three-dimensional spatial information; and achieving two-dimensional direction-of-arrival estimation through CANDECOMP/PARACFAC decomposition. The present disclosure constructs a processing framework of a structured coarray tensor based on statistical analysis of coprime planar array tensor signals, to achieve multi-source two-dimensional direction-of-arrival estimation in the underdetermined case on the basis of ensuring the performance such as resolution and estimation accuracy, and can be used for multi-target positioning.

Hybrid ranging may be provided. A coverage environment may be divided into a plurality of areas and a corresponding plurality accuracy gradients for each of the plurality of areas may be determined. Passive ranging may be implemented for ones of the plurality of areas that have a high accuracy gradient and one of a high client device density and low client device movement. Active ranging may be implemented for ones of the plurality of areas that have a low client device density. Based on at least one of a level of client device density and movement speed of client devices, switching may be performed between passive ranging and active ranging for ones of the plurality of areas that have at least one of high client device density and high client device movement.

Hybrid ranging may be provided. A coverage environment may be divided into a plurality of areas and a corresponding plurality accuracy gradients for each of the plurality of areas may be determined. Passive ranging may be implemented for ones of the plurality of areas that have a high accuracy gradient and one of a high client device density and low client device movement. Active ranging may be implemented for ones of the plurality of areas that have a low client device density. Based on at least one of a level of client device density and movement speed of client devices, switching may be performed between passive ranging and active ranging for ones of the plurality of areas that have at least one of high client device density and high client device movement.

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.

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 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.