A Space-Time Adaptive Processing Method Based on Sparse Reconstruction of Reverberation Interference

一种基于混响干扰稀疏重构的STAP方法

School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China

Received: 24 April 2020

Abstract

Aiming at the problem of how to obtain reverberation samples and estimate their covariance matrix in the space-time adaptive processing(STAP) of sonar system, a new space-time adaptive processing method is proposed based on sparse reconstruction of reverberation in this paper. Firstly, according to the space-time distribution characteristics of reverberation received by moving platform sonar, a space-time steering dictionary for sparse reconstruction of reverberation is designed along the relation curve between Doppler frequency shift and incident cone angle cosine of the reverberation unit. Then, a reverberation sample in the rangecell under test (RUT) is reconstructed with high precision by sparse decomposition of signals obtained from the sonar array in the space-time steering dictionary. Finally, based on the prior information of reverberation probability distribution model, a sufficient number of reverberation samples are generated to meet the requirement of performance loss index on reverberation sample size in the space-time adaptive processing, so as to correctly obtain estimation of the covariance matrix of reverberation. This method can reconstruct the reverberation samples and estimate the reverberation covariance matrix directly from the data in RUT without relying on the auxiliary data from units adjacent to the RUT. Therefore, it is not only suitable for the environment with constant reverberation statistical characteristics, but also suitable for the environment with varying statistical characteristics. Simulation results of sonar forward-looking array and side-looking array indicate that the improvement factor of the proposed method is about 10dB lower than the traditional space-time adaptive processing method. So this new STAP method has good anti-reverberation performance.

摘要

针对声呐空时自适应处理中混响样本获取及其协方差矩阵估计的难题，提出了一种基于混响稀疏重构的空时自适应处理方法。根据运动平台声呐混响在空时平面的分布规律，沿着混响单元多普勒频移与入射锥角余弦的关系曲线设计混响稀疏重构的空时导向字典；通过将声呐阵列观测数据在所设计的字典上进行稀疏分解，以较高精度重构了待检测距离单元中的混响样本；基于混响概率分布模型的先验信息，生成足够数量的混响样本以满足空时自适应处理中性能损失指标对混响样本数目的要求，从而得到混响协方差矩阵估计。该方法能够直接从待检测距离单元中重构混响样本，有效估计混响协方差矩阵，而不依赖于临近的辅助数据，因此不仅适合于混响统计特性不变的环境，也适合于统计特性变化的环境。声呐前视阵列、侧视阵列的仿真结果表明，该方法的改善因子比传统方法降低约10 dB，具有良好的抗混响性能。