Volume 13 Issue 5
Sep.  2024
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Article Navigation >  Journal of Radars  > 2024  >  13(5): 1123-1133
XIAO Jiong, TANG Bo, and WANG Hai. Sparse reconstruction-based direction of arrival estimation for MIMO radar in the presence of unknown mutual coupling[J]. Journal of Radars, 2024, 13(5): 1123–1133. doi: 10.12000/JR24061
Citation: XIAO Jiong, TANG Bo, and WANG Hai. Sparse reconstruction-based direction of arrival estimation for MIMO radar in the presence of unknown mutual coupling[J]. Journal of Radars, 2024, 13(5): 1123–1133. doi: 10.12000/JR24061
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Sparse Reconstruction-based Direction of Arrival Estimation for MIMO Radar in the Presence of Unknown Mutual Coupling

DOI: 10.12000/JR24061 CSTR: 32380.14.JR24061

  • College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China

Funds:  The National Natural Science Foundation of China (62171450), The Anhui Provincial Natural Science Foundation (2108085J30), Research Plan of National University of Defense Technology (23-ZZCX-JDZ-42)
More Information
  • Corresponding author: TANG Bo, tangbo06@gmail.com
  • Received Date: 2024-04-03
  • Rev Recd Date: 2024-06-23
    • Available Online: 2024-07-01
  • Publish Date: 2024-07-12
    Abstract
  • To improve the accuracy of Direction Of Arrival (DOA) estimation in Multiple Input Multiple Output (MIMO) radar systems under unknown mutual coupling, we propose a mutual coupling calibration and DOA estimation algorithm based on Sparse Learning via Iterative Minimization (SLIM). The proposed algorithm utilizes the spatial sparsity of target signals and estimates the spatial pseudo-spectra and the mutual coupling matrices of MIMO arrays through cyclic optimization. Moreover, it is hyperparameter-free and guarantees convergence. Numerical examples demonstrate that for MIMO radar systems under unknown mutual coupling conditions, the proposed algorithm can accurately estimate the DOA of targets with small angle separations and relatively high Signal-to-Noise Ratios (SNRs), even with a limited number of samples. In addition, low DOA estimation errors are achieved for targets with large angle separations and small sample sizes, even under low-SNR conditions.

     

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