Off-grid DOA Estimation Method Based on Covariance Matrix Reconstruction

Hongyan WANG; Ruonan YU; Mian Pan; Zumin WANG

doi:10.11999/JEIT200697

Volume 43 Issue 10

Oct. 2021

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Hongyan WANG, Ruonan YU, Mian Pan, Zumin WANG. Off-grid DOA Estimation Method Based on Covariance Matrix Reconstruction[J]. Journal of Electronics & Information Technology, 2021, 43(10): 2863-2870. doi: 10.11999/JEIT200697

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Hongyan WANG, Ruonan YU, Mian Pan, Zumin WANG. Off-grid DOA Estimation Method Based on Covariance Matrix Reconstruction[J]. Journal of Electronics & Information Technology, 2021, 43(10): 2863-2870. doi: 10.11999/JEIT200697

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Off-grid DOA Estimation Method Based on Covariance Matrix Reconstruction

doi: 10.11999/JEIT200697

Hongyan WANG^{1, 2, 3},
Ruonan YU²,
Mian Pan⁴,
Zumin WANG^{2
,
,}

1.
School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
2.
College of Information Engineering, Dalian University, Dalian 116622, China
3.
Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, China
4.
School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310018, China

Funds: The National Natural Science Foundation of China(61301258, 61271379), China Postdoctoral Science Foundation(2016M590218), The Key Projects of Natural Science Foundation of Zhejiang Province (LZ21F010002)

Received Date: 2020-12-15
Rev Recd Date: 2020-12-23

Available Online: 2021-02-27

Publish Date: 2021-10-18

Abstract

Abstract

Focusing on the problem of rather large estimation error in Direction Of Arrival (DOA) estimation caused by grid mismatch in the sparse representation model, an Off-Grid DOA estimation method based on Covariance Matrix Reconstruction (OGCMR) is proposed. Firstly, the offset between the DOA and the grid points is incorporated into the constructed spatial discrete sparse representation model of the received data; After that, based on the reconstructed signal covariance matrix, a sparse representation convex optimization problem associated with DOA estimation can be established; Subsequently, a sampling covariance matrix estimation error convex model is constructed, and then this convex set can be explicitly included into the sparse representation model to improve the performance of sparse signal reconstruction; Finally, an alternating optimization method can be exploited to solve the resultant joint optimization problem to acquire the grid offset parameters as well as the off-grid DOA estimation. Numerical simulations show that, compared with the traditional conventional MUltiple SIgnal Classification(MUSIC), L1-SVD, Sparse and Low-Rank Decomposition based Robust MVDR (SLRD-RMVDR) algorithms and so on, the proposed algorithm has rather better angular resolution and higher DOA estimation accuracy.
- Direction Of Arrival (DOA),
- Off-grid,
- Sparse representation,
- Convex optimization

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References(24)

References

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