拖线阵畸变阵形和目标方位估计算法

Distorted Array Shape and Target Azimuth Estimation Algorithm for Towed Arrays

摘要:

拖线阵声呐广泛应用于水下目标检测，在舰艇平台转向机动时，柔性的拖线阵不再保持直线形， 阵形发生畸变，阵元位置未知，导向矢量失配，进而导致拖线阵声呐性能急剧下降。针对这一问题，提出了一种基于稀疏贝叶斯学习（Sparse Bayesian Learning，SBL）的阵形和目标方位估计算法。该算法仅利用接收到的声学信号估计目标方位，将转弯时的畸变阵形建模为抛物线形，并将抛物线参数作为 SBL 的超参数，实现了目标方位和畸变阵形的同时估计。仿真分析结果表明：在畸变阵形为抛物线模型的假设下，算法可以同时对畸变阵形和目标方位进行估计，并具有更好的目标检测能力和更高的分辨力。

Abstract:

Towed array sonar is widely used in underwater target detection. During ship platform maneuvering，the towed array shape is distorted，deviating from linear configuration. This results in unknown array elements positions and mismatch of steering vector，leading to significant performance degradation of the sonar. To solve this problem，the shape and azimuth estimation algorithm based on Sparse Bayesian Learning is proposed. The algorithm uses received acoustic data to estimate target azimuth，models the distorted array shape during maneuvering as a parabolic curve，and takes the parabolic parameters as hyper-parameters in the SBL framework to realize simultaneous estimation of both array shape parameters and target azimuths. Simulation results demonstrate that under the assumption of parabolic distortion model，the proposed algorithm achieves concurrent estimation of array distortion and target azimuths，exhibiting enhanced target detection capability and superior resolution.