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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

Pending Publication Date: 2020-09-25
ZHEJIANG SCI-TECH UNIV
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Problems solved by technology

[0004] Aiming at the problem of large estimation errors in the direction of arrival (DOA) estimation caused by grid mismatch in the sparse representation model, the present invention proposes an off-grid (off-grid) DOA estimation method based on covariance matrix reconstruction

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  • Off-grid DOA estimation method based on covariance matrix reconstruction
  • Off-grid DOA estimation method based on covariance matrix reconstruction
  • Off-grid DOA estimation method based on covariance matrix reconstruction

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[0099] Attached below figure 1 The implementation steps of the present invention are further described in detail:

[0100] The present invention proposes a discrete grid DOA estimation method (Off-Grid based on Covariance Matrix Reconstruction, OGCMR) based on covariance matrix reconstruction under grid mismatch conditions. First, the offset between the DOA and the grid point is included in the sparse representation model of the received data; then the sparse representation model for DOA estimation is established based on the reconstructed signal covariance matrix; then, the sampling covariance matrix estimation error convex model is constructed, And based on the statistical characteristics that the sampling covariance matrix estimation error obeys the asymptotic normal distribution, the upper bound of the estimation error is derived, and then the convex set is explicitly included in the sparse representation model to improve the sparse signal reconstruction performance and im...

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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.

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...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S3/14
CPCG01S3/14
Inventor 王洪雁于若男薛喜扬汪祖民
Owner ZHEJIANG SCI-TECH UNIV
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