Abstract As one of the most important physical phenomena of underwater acoustics, ocean reverberation is a common and strong interference which significantly degrades the performance of target bearing estimation. Meanwhile, sensor failure is inevitable in actual sonar deployment as the underwater scene is complicated. Therefore, it is a challenge for acoustic localization in the ocean reverberation with sensor failure. To address this issue, we propose an improved approach based on the principle of low rank characteristics of matrix in this paper. Firstly, we utilize Hankel structured matrix to counteract the problem of sensor failure. Then, the algorithm of matrix completion (MC) based on l 1 -norm and l 2 -norm are exploited to recover the true signal matrix from the corrupted received matrix. Numerical results demonstrate that compared with other related methods, the l 1 -norm provides better capability in the probability of recovery and shows the best robustness of bearing estimation with the narrow beam width and low sidelobe. Moreover, the proposed approach verifies the performances of reverberation suppression and achieves high resolution of localization.

中文翻译：

---

通过传感器故障的矩阵完成在海洋混响中的声学定位

摘要 作为水声学中最重要的物理现象之一，海洋混响是一种常见且强烈的干扰，显着降低了目标方位估计的性能。同时，由于水下场景复杂，在实际声纳部署中，传感器故障在所难免。因此，在传感器故障的海洋混响中进行声学定位是一个挑战。为了解决这个问题，我们提出了一种基于矩阵低秩特征原理的改进方法。首先，我们利用 Hankel 结构矩阵来解决传感器故障的问题。然后，利用基于l 1 -范数和l 2 -范数的矩阵补全(MC)算法从损坏的接收矩阵中恢复真实的信号矩阵。数值结果表明，与其他相关方法相比，l 1 -范数提供了更好的恢复概率能力，并在窄波束宽度和低旁瓣下显示了最佳的方位估计鲁棒性。此外，所提出的方法验证了混响抑制的性能并实现了定位的高分辨率。