Fan Qing, Yu Yun, An Liang, Cao Hongli, Zhu Chuanqi
Key Laboratory of Underwater Acoustic Signal Processing (Southeast University), Ministry of Education, Nanjing 210096, China.
Naval Research Academy, People's Liberation Army, Beijing 100161, China.
J Acoust Soc Am. 2023 Dec 1;154(6):3810-3820. doi: 10.1121/10.0023934.
Wideband sparse spatial spectrum estimation is an important direction-of-arrival (DOA) estimation method that can obtain a high resolution with few snapshots and a low signal-to-noise ratio. However, in an underwater strong interference environment, the accuracy of DOA estimation may be seriously affected, and even the weak targets could be completely masked. In this paper, we propose a fast matrix filter design method based on truncated nuclear norm regularization to attenuate strong interferences while passing weak targets. The matrix filter operator and the exact covariance matrix after filtering can be obtained simultaneously by solving a convex optimization problem that contains the output power term and non-Toeplitz error propagation control term. Then the modified sparse spectrum fitting algorithm based on the matrix filter is used to estimate spatial spectrum over closely spaced wideband signals. Compared with existing methods, the proposed method achieves higher DOA estimation accuracy and lower computational time for matrix filter design. Meanwhile, the estimation accuracy of the proposed method is verified with the experimental results.
宽带稀疏空间谱估计是一种重要的波达方向（DOA）估计方法，它能够在较少快照和低信噪比的情况下获得高分辨率。然而，在水下强干扰环境中，DOA估计的准确性可能会受到严重影响，甚至弱目标可能会被完全掩盖。在本文中，我们提出了一种基于截断核范数正则化的快速矩阵滤波器设计方法，以在通过弱目标的同时衰减强干扰。通过求解一个包含输出功率项和非托普利兹误差传播控制项的凸优化问题，可以同时获得矩阵滤波器算子和滤波后的精确协方差矩阵。然后，基于矩阵滤波器的改进稀疏谱拟合算法用于估计紧密间隔宽带信号的空间谱。与现有方法相比，该方法在矩阵滤波器设计方面实现了更高的DOA估计精度和更低的计算时间。同时，实验结果验证了该方法的估计精度。
