基于时频分析的海杂波背景下舰船目标检测

为了克服基于小波尺度谱重排的时频分析方法中时、频分辨率不佳及时频分布可读性较差等问题，本文提出了一种基于参数优化Morlet小波变换和奇异值分解的海杂波背景下舰船目标检测算法。算法首先利用Shannon小波熵作为目标函数，根据高频地波雷达信号的特点自适应地优化Morlet小波变换的时间带宽积参数，使得后续重排尺度谱的时、频分辨率同时达到最佳。然后再对重排小波尺度谱进行基于奇异值分解的降噪处理，以抑制环境噪声的影响，进一步提高时频分布的可读性。实验结果表明：与传统的时频分析算法相比，本文提出的算法具有更好的时频聚集性和较强的噪声抑制能力，能有效地检测海杂波背景下缓慢运动的匀速和匀加速舰船目标。

Ship target detection under sea clutter background based on time-frequency analysis

In order to overcome the problems of poor time-frequency resolution and inadequate readability of time-frequency distribution in the reassigned wavelet scalogram based method of time frequency analysis, this paper presents a new algorithm for ship target detection under sea clutter background based on parameter optimized Morlet wavelet transform and Singular Value Decomposition(SVD). Firstly,according to the signal characteristic of High Frequency Surface Wave Radar (HFSWR), the Time-Bandwidth Product (TBP) of Morlet wavelet basis is adaptively optimized by using Shannon entropy as an objective function, thus allowing both the time resolution and the frequency resolution of the subsequent reassigned wavelet scalogram to be optimal simultaneously. Then, Singular Value Decomposition based de-noising is applied to the reassigned wavelet scalogram to reduce the interference influence of environmental noises and improve the readability of time-frequency distribution. The experimental results show that the proposed algorithm has better time-frequency concentration and better noise restraining ability than those of traditional time-frequency analysis algorithms, which can effectively detect the slow-moving ship targets at constant speed or at constant acceleration under strong sea clutter background.