一种基于SPWVD-WVD的高质量时频分析方法及ISAR成像应用

交叉项干扰抑制与高时频聚集度是准确反应信号的时频分布特征的重要因素。传统的魏格纳-维尔分布(Wigner-Ville Distribution,WVD)算法虽能获得较高的时频分辨特征，但分析多成分信号时存在严重的交叉项干扰问题，限制了其实用性；而平滑伪魏格纳-维尔分布(Smoothed Pseudo Wigner-Ville Distribution,SPWVD)算法虽在一定程度上抑制交叉项干扰，但降低了时频聚集度。为了解决上述问题，提出了基于SPWVD-WVD的时频分析方法。该方法利用SPWVD与WVD之间的滤波互消效应，将SPWVD二值化结果与WVD结果进行矩阵运算，最终得到高质量的时频分析结果。实验结果表明，所提出的算法能够有效去除多分量信号的交叉项干扰，提高信号分析结果的时频聚集度，还原多分量信号的真实时频分布。最后将该算法成功应用于逆合成孔径雷达成像中。

A High-quality Time-Frequency Analysis Method Based on SPWVD-WVD and Its Application in ISAR Imaging

The cross-term interference suppression and high time-frequency aggregation are important factors to accurately reflect the time-frequency distribution characteristics of signals.The traditional Wigner-Ville Distribution(WVD) algorithm can obtain high time-frequency resolution characteristics,but there are serious cross-term interference problems when analyzing multi-component signals,which limits its practicability.The Smoothed Pseudo Wigner-Ville Distribution(SPWVD) algorithm suppresses the cross term interference to a certain extent,but reduces the time-frequency aggregation.To solve above problems,a time-frequency analysis method based on SPWVD-WVD is proposed.This method utilizes the filtering mutual cancellation effect between SPWVD and WVD,performs matrix operation between SPWVD binarization results and WVD results,and finally obtains high-quality time-frequency analysis results.The experimental results show that the proposed algorithm can effectively remove the cross-term interference of multi-component signals,improve the time-frequency aggregation of signal analysis results,and restore the real time-frequency distribution of multi-component signals.Finally,the algorithm is successfully applied in inverse synthetic aperture radar(ISAR) imaging.