Two-dimensional DOA Estimation for Low-angle Target Based on ADMM

MA Jianjun; WEI Shaopeng; MA Hui; LIU Hongwei

doi:10.11999/JEIT210582

Volume 44 Issue 8

Aug. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(8): 2859-2866

MA Jianjun, WEI Shaopeng, MA Hui, LIU Hongwei. Two-dimensional DOA Estimation for Low-angle Target Based on ADMM[J]. Journal of Electronics & Information Technology, 2022, 44(8): 2859-2866. doi: 10.11999/JEIT210582

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MA Jianjun, WEI Shaopeng, MA Hui, LIU Hongwei. Two-dimensional DOA Estimation for Low-angle Target Based on ADMM[J]. Journal of Electronics & Information Technology, 2022, 44(8): 2859-2866. doi: 10.11999/JEIT210582

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Two-dimensional DOA Estimation for Low-angle Target Based on ADMM

doi: 10.11999/JEIT210582 cstr: 32379.14.JEIT210582

National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

Funds: The Youth Fund of National Natural Science Foundation of China (61901344), The Program of Subject Innovation and Introduction in Colleges and Universities (B18039), The Postdoctoral Innovative Talents Support Program (BX20180239)

Received Date: 2021-06-16
Accepted Date: 2022-03-07
Rev Recd Date: 2022-01-18

Available Online: 2022-03-15

Publish Date: 2022-08-17

Abstract

Abstract

For the two-dimensional DOA estimation problem of low elevation target of Very High Frequency (VHF) array radar, a fast two-dimensional algorithm based on Alternating Direction Method of Multipliers (ADMM) is proposed. Firstly, the two-dimensional DOA estimation problem is transformed into two one-dimensional DOA problems by using the uncoupled characteristics of azimuth and elevation under uniformed planar array, and the target information is extracted by azimuth and elevation dimensional digital beamforming, and then based on signal mode, the over-complete expression in the signal space domain is established. Finally, the ADMM algorithm is used to estimate azimuth and elevation. ADMM algorithm avoids the complicated calculation of two-dimension joint estimation, reduces greatly the complexity, and the algorithm process does not need the eigenvalue decomposition, which improves further the operation efficiency. Simulation results show the superiority of the algorithm.

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References(18)

References

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