内孤立波环境下稳健降阶自适应匹配场定位方法研究

海洋内孤立波的存在会引起海水混合，温盐结构改变，从而导致声速在时间和空间上的不均匀分布，而在匹配场定位中则表现为声速失配，定位不准确。该文提出一种内孤立波下稳健的降阶自适应匹配场定位方法(RR-AMFP)。在传统自适应匹配场定位算法的基础上，融合了主分量抑制波束形成方法，通过特征分解对拷贝协方差矩阵进行降阶，抑制噪声空间，同时采用抑制系数和权重因子来计算匹配过程中的权向量，监测失配的拷贝向量。因此该算法可以在内孤立波环境下保持更好的稳健性，而且阶数的降低也缩短了计算时间。仿真结果表明:该算法可以实现单个内孤立波下的准确定位，但大振幅的内孤立波波列仍然会造成较大的定位误差。在南海开展了内孤立波环境下的声源定位实验，估计距离误差为3.7%，深度误差为1.6%，验证了该算法在实际海洋内孤立波环境下的有效性。

Robust Adaptive Matched Field Processing of Rank Reduction for Source Localization under Internal Solitary Waves

The mismatch of sound speed induced by internal solitary waves will cause the inaccurate estimation in the matched field processing for source localization. In this paper, a robust Adaptive Matched Field Processing method of Rank Reduction (RR-AMFP) for internal solitary waves is proposed. Based on the traditional adaptive matched field processing algorithm, this method integrates dominant mode rejection beamforming, and reduces the rank of sampling covariance matrix by eigen-decomposition, and suppresses the noise space. Meanwhile, a suppressing coefficient and a weighting factor are used to calculate the weight vector in the matching process, and the mismatched coping vectors are detected. Therefore, this method can maintain better robustness in the internal solitary wave environment, and the reduction of rank also shortens the calculation time. The simulation results show that this method can accurately estimate the source location under a single internal solitary wave, but the internal solitary wave train with large amplitude will still lead to more errors of estimation. The estimated distance error is 3.3% and depth error is 1.5% in the localization experiment of internal solitary waves in the South China Sea, which belongs to reliable localization. The experiment results demonstrate the effectiveness of the method in the actual environment with internal solitary waves.