Ma Jitong, Zhang Jiacheng, Yang Zhengyan, Qiu Tianshuang
College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China.
Department of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China.
Sensors (Basel). 2022 Aug 20;22(16):6268. doi: 10.3390/s22166268.
Direction of arrival (DOA) estimation is an essential and fundamental part of array signal processing, which has been widely used in radio monitoring, autonomous driving of vehicles, intelligent navigation, etc. However, it remains a challenge to accurately estimate DOA for multiple-input multiple-output (MIMO) radar in impulsive noise environments. To address this problem, an off-grid DOA estimation method for monostatic MIMO radar is proposed to deal with non-circular signals under impulsive noise. In the proposed method, firstly, based on the property of non-circular signal and array structure, a virtual array output was built and a real-valued sparse representation for the signal model was constructed. Then, an off-grid sparse Bayesian learning (SBL) framework is proposed and further applied to the virtual array to construct novel off-grid sparse model. Finally, off-grid DOA estimation was realized through the solution of the sparse reconstruction with high accuracy even in impulsive noise. Numerous simulations were performed to compare the algorithm with existing methods. Simulation results verify that the proposed off-grid DOA method enables evident performance improvement in terms of accuracy and robustness compared with other works on impulsive noise.
到达方向（DOA）估计是阵列信号处理的一个重要且基础的部分，已广泛应用于无线电监测、车辆自动驾驶、智能导航等领域。然而，在脉冲噪声环境下准确估计多输入多输出（MIMO）雷达的DOA仍然是一个挑战。为了解决这个问题，提出了一种用于单基地MIMO雷达的非网格DOA估计方法，以处理脉冲噪声下的非循环信号。在所提出的方法中，首先，基于非循环信号的特性和阵列结构，构建了虚拟阵列输出，并为信号模型构建了实值稀疏表示。然后，提出了一种非网格稀疏贝叶斯学习（SBL）框架，并将其进一步应用于虚拟阵列以构建新颖的非网格稀疏模型。最后，即使在脉冲噪声中，也通过高精度的稀疏重构解实现了非网格DOA估计。进行了大量仿真以将该算法与现有方法进行比较。仿真结果验证，与其他关于脉冲噪声的工作相比，所提出的非网格DOA方法在准确性和鲁棒性方面实现了明显的性能提升。
