L-Shaped Coprime Array DOA Estimation with Tensor Denoising
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Solution Overview
Problem
Existing methods for direction of arrival estimation in coprime arrays suffer from noise interference and loss of structural information due to noise autocorrelation and high-order sampling noise, limiting the effectiveness of tensor signal processing and virtual domain expansion.
Innovation Solution
A method for estimating direction of arrival using a sub-array partition type L-shaped coprime array based on fourth-order sampling covariance tensor denoising, involving high-order tensor statistics denoising and structured virtual domain signal processing to extract accurate two-dimensional direction information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional virtual domain derivation method based on higher-order signal statistics is used, then direction of arrival estimation can be achieved, but noise power and high-order sampling noise introduce serious interference
Solution Approach 1:
The patent extracts and removes the harmful noise components from the fourth-order sampling covariance tensor through denoising processing. Specifically, it separates the signal-containing components from the noise components (including noise autocorrelation and high-order sampling noise) and eliminates the noise parts, thereby resolving the contradiction between achieving direction of arrival estimation and avoiding noise interference.
Solution Approach 2:
The patent converts the harmful noise interference into a beneficial filtering process. By utilizing the statistical characteristics of the noise and signal, the denoising method transforms the noisy fourth-order sampling covariance tensor into a clean virtual domain tensor, where the noise components are converted into removable artifacts that can be systematically eliminated through the proposed denoising algorithm.
2Ease of operation
If vectorizing received signal covariance matrix is used to derive virtual domain signal, then direction of arrival estimation can be realized, but original structural information of received signal is lost
Solution Approach 1:
The patent transitions from traditional vector-based second-order statistics to tensor-based fourth-order statistics. By elevating the data structure from vectors to fourth-order tensors, the method preserves the multi-dimensional structural information of the received signal while enabling virtual domain expansion. This dimensional upgrade allows the covariance tensor to maintain spatial and temporal correlations that would be lost in vectorization.
Solution Approach 2:
The patent constructs a composite virtual domain tensor that integrates multiple types of information (spatial correlations, temporal correlations, and higher-order statistics) into a unified structure. This composite tensor combines the advantages of different statistical orders and maintains the intricate structural relationships among array elements, signal sources, and noise components.
Data Source
AI summary
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.