L-Type Coprime Array Direction of Arrival Estimation
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Solution Overview
Problem
Existing methods for direction of arrival estimation in multi-dimensional coprime arrays suffer from signal structure damage and loss of virtual domain signal correlation information, particularly when dealing with sparse signals and their virtual domain expansion, leading to performance losses and ineffective feature extraction.
Innovation Solution
The method employs coupled tensor decomposition to establish a relationship between the L-type coprime array's augmented virtual domain and tensor signal modeling, preserving structural information and utilizing correlation between virtual domain tensors for high-precision two-dimensional direction of arrival estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If vectorization method is used to derive virtual domain signal from received signal covariance matrix, then direction of arrival estimation can be realized based on virtual domain signal processing, but structural information of the received signal is lost and virtual domain signal model has structural damage and excessive linear scale
Solution Approach 1:
The patent transforms the received signal from a vector representation to a tensor representation with multiple dimensions. Specifically, it constructs a fourth-order covariance tensor from the received signal, which preserves the multi-dimensional spatial-temporal structure information that is lost in vectorization. This dimensional transformation enables both structural information preservation and effective direction of arrival estimation.
2Reliability
If spatial smoothing is applied to divide virtual domain signal, then full-rank virtual domain signal statistics can be obtained, but spatial correlation property between divided virtual domain signals is ignored and performance loss occurs
Solution Approach 1:
The patent merges multiple virtual domain signals into a single fourth-order covariance tensor that preserves spatial correlation properties. Instead of dividing the virtual domain signal into separate components for processing, it combines them into a unified tensor structure that maintains the spatial correlation relationships, thereby achieving full-rank statistics without losing spatial correlation information.
Solution Approach 2:
The patent creates a composite fourth-order covariance tensor that integrates multiple virtual domain signals and their spatial correlation properties into a single structured object. This composite tensor structure simultaneously provides full-rank statistics and preserves spatial correlation, resolving the contradiction between these two requirements.
3Measurement precision
If traditional tensor signal processing methods are used, then high-precision direction of arrival estimation can be achieved, but they are only effective under matching Nyquist sampling rate and have not involved statistical analysis of coprime array sparse signals
Solution Approach 1:
The patent develops a universal fourth-order covariance tensor model that can handle both the sparse signal characteristics of coprime arrays and the direction of arrival estimation requirements. This tensor model is specifically designed to work with the statistical properties of coprime array signals while maintaining the ability to achieve high-precision direction of arrival estimation, thereby extending the applicability of tensor methods to this specific signal type.
Data Source
AI summary
The disclosure provides a method for estimating a direction of arrival of an L-type coprime array based on coupled tensor decomposition. The method includes: constructing an L-type coprime array with separated sub-arrays and modeling a received signal; deriving a fourth-order covariance tensor of the received signal of the L-type coprime array; deriving a fourth-order virtual domain signal corresponding to an augmented virtual uniform cross array; dividing the virtual uniform cross array by translation; constructing a coupled virtual domain tensor by stacking a translation virtual domain signal; and obtaining a direction of arrival estimation result by coupled virtual domain tensor decomposition. The present invention makes full use of the spatial correlation property of the virtual domain tensor statistics of the constructed L-type coprime array with the separated sub-arrays, and realizes high-precision two-dimensional direction of arrival estimation by coupling the virtual domain tensor processing, which can be used for target positioning.