A Fast Direction Estimation Algorithm Based on Vector Hydrophone Array under Non-ideal Conditions

Biao WANG; Yu CHEN; Qianchi XU; Shijie GAO; Cen ZHANG

doi:10.11999/JEIT200541

Volume 43 Issue 3

Mar. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(3): 745-751

Biao WANG, Yu CHEN, Qianchi XU, Shijie GAO, Cen ZHANG. A Fast Direction Estimation Algorithm Based on Vector Hydrophone Array under Non-ideal Conditions[J]. Journal of Electronics & Information Technology, 2021, 43(3): 745-751. doi: 10.11999/JEIT200541

Citation:

Biao WANG, Yu CHEN, Qianchi XU, Shijie GAO, Cen ZHANG. A Fast Direction Estimation Algorithm Based on Vector Hydrophone Array under Non-ideal Conditions[J]. Journal of Electronics & Information Technology, 2021, 43(3): 745-751. doi: 10.11999/JEIT200541

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A Fast Direction Estimation Algorithm Based on Vector Hydrophone Array under Non-ideal Conditions

doi: 10.11999/JEIT200541

1.
Jiangsu University of Science and Technology, Zhenjiang 212002, China
2.
Jiangsu Zhonghaida Ocean Information Technology Co., Ltd., Nanjing 211800, China

Funds: The National Natural Science Foundation of China (52071164)

Received Date: 2020-07-02
Rev Recd Date: 2020-12-14

Available Online: 2020-12-31

Publish Date: 2021-03-22

Abstract

Abstract

In order to realize the fast direction estimation of underwater targets under the conditions of less snapshot and low SNR, a sparse decomposition model of vector hydrophone array direction estimation is established. The real value conversion technique is used to convert the complex direction matrix into the real number field, so as to reconstruct the sparse signal matrix using the SL0 algorithm to obtain the orientation estimation result. The SL0 algorithm is improved, the Compound Inverse Proportional Function (CIPF) with better convergence is used as a smoothing function, and a weighted method is proposed which can promote sparsity, the weighted method is used to correct the problem that the norm as the initial iteration value deviates far from the sparse solution to increase the speed of azimuth estimation. The simulation verifies that the proposed algorithm can achieve better performance than the traditional subspace algorithm under the conditions of low snapshot and low SNR, and improve the speed of bearing estimation while ensuring performance.
- Vector hydrophone,
- DOA estimate,
- Sparse decomposition

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References(13)

References

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