基于EMD-RSPWVD算法的SAR目标运动参数仿真分析与应用研究

Simulation Analysis and Application of SAR Target Motion Parameters based on EMD-RSPWVD Algorithm

针对传统时频分析方法处理多分量SAR运动目标回波数据时出现的交叉项影响严重和时频聚集性差等问题,提出一种融合改进的经验模式分解(Empirical Mode Decomposition,EMD)算法和重排平滑伪维格纳维尔分布(Reassigned Smoothing Pseudo-Wigner-Ville Distribution,RSPWVD)算法的新时频分析算法--EMD-RSPWVD算法。利用改进的EMD算法将多分量SAR动目标回波信号分解为彼此独立信号分量,然后对独立分量分别做基于RSPWVD算法的时频分析,以消除交叉项和获得高的时间-频率分辨率。分别利用模拟回波信号数据和真实回波信号数据,探究该算法对于多分量SAR运动回波数据的分析性能。结果表明,该算法具有良好的抗噪性和运动目标检测能力,以及高精度的运动参数估计性能。

When processing multi-component SAR moving target echo data by traditional time-frequency analysis method,there is serious cross-term influence and poor time-frequency clustering. A new time-frequency analysis algorithm named EMD-RSPWVD is proposed. It combines the improved Empirical Mode Decomposition(EMD)algorithm and Reassigned Smoothing Pseudo-Wigner-Ville Distribution(RSPWVD)algorithm.The improved EMD algorithm is used to decompose the multi-component SAR moving target echo signal into independent signal components. Then the time-frequency analysis of independent components which based on RSPWVD algorithm is performed to eliminate cross-terms and obtain high time-frequency resolution. Finally,simulated echo data and real echo data are used to analyze the performance of this algorithm for multi-component SAR motion echo data. The results show that the algorithm has good anti-noise ability,moving target detection ability and high-precision motion parameter estimation performance.