Off-grid DOA estimation method based on covariance matrix reconstruction

A technology of covariance matrix and matrix estimation, applied in the field of signal processing, can solve problems such as large estimation error of direction of arrival
CN111707985APending Publication Date: 2020-09-25ZHEJIANG SCI-TECH UNIV
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
ZHEJIANG SCI-TECH UNIV
Publication Date
2020-09-25

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Abstract

The invention relates to an off-grid DOA estimation method based on covariance matrix reconstruction, which belongs to the field of signal processing, and aims to solve the problem that DOA estimationhas large estimation errors due to grid mismatch in a sparse representation model. The DOA estimation method is characterized by comprising the following steps of firstly, including offset between DOA and grid points into a constructed received data airspace discrete sparse representation model, establishing a sparse representation convex optimization problem about DOA estimation based on the reconstructed signal covariance matrix, constructing a sampling covariance matrix estimation error convex model, and including the convex set explicit expression into a sparse representation model to improve the sparse signal reconstruction performance, and finally, solving the obtained joint optimization problem by adopting an alternate iteration method to obtain a grid offset parameter and off-gridDOA estimation. The method has the effects of relatively good angle resolution and relatively high DOA estimation precision.
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Description

technical field

[0001] The invention belongs to the field of signal processing, in particular to an off-grid DOA estimation method based on covariance matrix reconstruction. Background technique

[0002] As one of the research hotspots in the field of array signal processing, Direction Of Arrival (DOA) estimation technology has been widely used in wireless communication, target tracking, speech processing, radar and radio astronomy and other fields. With the deepening of DOA estimation theory research, various DOA estimation methods have been proposed one after another. Classical subspace methods can achieve super-resolution direction finding, such as multiple signal classification (MUltiple SIgnal Classification, MUSIC), rotation invariant subspace (Estimation of Signal Parameters via Rotational Invariance Technique, ESPRIT), etc., but in low SNR Or under the condition of insufficient number of snapshots, its estimation performance will drop significantly. To solve this p...

Claims

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