| 引用本文: | 陈金立，张程，陈宣，李家强. 阵元失效下基于矩阵重构的MIMO雷达DOA估计[J]. 雷达科学与技术, 2022, 20(5): 524-530.[点击复制] |
| CHEN Jinli, ZHANG Cheng, CHEN Xuan, LI Jiaqiang. DOA Estimation Based on Matrix Reconstruction in MIMO Radar Under Array Element Failures[J]. Radar Science and Technology, 2022, 20(5): 524-530.[点击复制] |
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| 摘要: |
| 针对阵元失效下MIMO雷达目标DOA估计性能下降问题，提出一种基于虚拟阵列采样数据矩阵重构的MIMO雷达DOA估计方法。MIMO雷达的阵元失效分为冗余虚拟阵元失效和非冗余虚拟阵元失效两种情况。当冗余虚拟阵元失效时，通过合并空间上位置相同的正常冗余虚拟阵元输出数据以实现信号降维与失效阵元数据填充。当非冗余虚拟阵元失效时，经降维填充后的数据矩阵中仍存在整行缺失数据，根据降维数据矩阵的低秩和稀疏先验，建立带低秩和稀疏约束的矩阵填充模型，并利用ALM-ADMM算法求解来恢复完整的降维数据矩阵。最后利用root-MUSIC算法估计目标DOA。仿真结果表明，本文方法能够有效提高MIMO雷达在阵元失效时的DOA估计精度。 |
| 关键词: MIMO雷达 阵元失效 DOA估计 ALM-ADMM算法 |
| DOI：DOI:10.3969/j.issn.1672-2337.2022.05.008 |
| 分类号:TN911.23;TN958 |
| 基金项目:国家自然科学基金(No.62071238， 61801231); 江苏省自然科学基金(No.BK20191399) |
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| DOA Estimation Based on Matrix Reconstruction in MIMO Radar Under Array Element Failures |
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CHEN Jinli, ZHANG Cheng, CHEN Xuan, LI Jiaqiang
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1. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China;2. School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China;3. School of Physics and Optoelectronic Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
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| Abstract: |
| Aiming at the performance degradation of direction of arrival (DOA) estimation for multiple-input multiple-output (MIMO) radar with array element failures, a DOA estimation method based on virtual array sampled data matrix reconstruction is proposed. The array element failure scenarios of MIMO radar can be divided into redundant virtual array element failures and non-redundant virtual array element failures. For redundant virtual array sensor failures, the output data of normal redundant virtual array elements with the same position in space are combined to achieve signal dimension reduction and fill the positions of the failed physical array elements. For the non-redundant virtual array element failure scenario, the whole row of missing data still exists in the reduced-dimension sampled data matrix obtained by using the redundancy of virtual array elements. Inspired by the facts that the reduced-dimension data has the low-rank and sparsity priors, a joint low-rank and sparsity prior’s constrained matrix completion model is established. The augmented Lagrange method-alternating direction method of multipliers (ALM-ADMM) algorithm is exploited to solve this optimization problem, and the missing data in the reduced-dimension sampled data matrix can be recovered perfectly. Finally, the root-MUSIC algorithm is employed to estimate the DOAs from the full reconstructed data matrix. Simulation results validate that the proposed method can effectively improve the DOA estimation accuracy of MIMO radar under array element failures. |
| Key words: MIMO radar array element failure DOA estimation ALM-ADMM algorithm |
