Systems, methods, and apparatus for processing signals using a sensor arrays are disclosed. In one aspect, an apparatus comprising a sensor array and a computing device is provided. The computing device may comprise one or more processors configured to generate a data matrix based on one or more signals received at each of the plurality of sensor and to determine a covariance matrix based on the data matrix. The one or more processors may also be configured to decompose the covariance matrix into a matrix of eigenvalues and a matrix of eigenvectors and to determine a projected data matrix based on applying the data matrix to the eigenvector matrix. Further, the one or more processors may be configured to determine a denoised projected data matrix based on denoising the projected data matrix and to determine a denoised data matrix based on the denoised projected data matrix.

There are included signal receiving units to acquire complex signal vectors of signals received by satellites, a correlation processing unit to calculate information of TDOA and FDOA by correlation processing between relevant complex signal vectors, a geolocation point calculating unit to calculate a geolocation point corresponding to the TDOA and the FDOA, a coordinate converting unit to convert the geolocation points into information of latitude and longitude, a frequency distribution calculating unit to calculate a frequency distribution of the geolocation point on a basis of the information of latitude and longitude, a geolocation point extracting unit to extract a geolocation point included in an area having a maximum frequency from the frequency distribution on a basis of the calculated frequency distribution, and a position estimating unit to estimate a position of a target radio wave source on a basis of the extracted geolocation point.

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. 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. The present invention makes full use of the statistical distribution characteristics of the high-order tensor of the constructed sub-array partition type L-shaped coprime array, realizes high-precision two-dimensional direction of arrival estimation through denoised virtual domain tensor signal processing, and can be used for target positioning.

A method comprises: by a DF antenna array, receiving a modulated RF signal and converting the modulated RF signal to multiple modulated RF channels; converting the multiple modulated RF channels to multiple channels of in-phase (I) and quadrature (Q) (I/Q) data; by a side-channel antenna, converting the modulated RF signal to a modulated RF side-channel; converting the modulated RF side-channel to a side-channel of I/Q data; demodulating and decoding the side-channel of the I/Q data to yield demodulated data; remodulating the demodulated data to produce remodulated reference I/Q data; correlating the remodulated reference I/Q data against the multiple channels of the I/Q data to produce correlation results for the multiple channels of the I/Q data; and determining an angle-of-arrival of the modulated RF signal to the DF antenna array based on the correlation results.