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中文摘要：
针对杂波背景的微弱动目标检测问题，提出了一种应用小波包变换的分数阶Fourier域动目标检测算法。算法采用最小Shannon熵标准确定最优小波树，利用阈值删除技术，对杂波背景的参数精确估计，从而对不同频段信号进行滤波。建立了FRFT域的动目标检测模型，采用似然比准则设计检测器，抑制杂波后的信号在FRFT域形成检测统计量，门限比较后判断信号的有无。仿真得出了在高斯杂波和实测海杂波背景下的检测性能曲线，性能接近匹配滤波器，结果表明算法能够在低信杂比环境下有效检测出动目标信号。
英文摘要：
A new weak moving target detection method in clutter background was proposed based on wavelet packet transform with fractional Fourier transform（WPT-FRFT）. Optimal wavelet tree was calculated using minimum Shannon entropy and threshold censored method was used to estimate clutter background parameters. On the basis of the moving target detection model in FRFT domain, a detector was designed according to likelihood ratio criterion. Clutter was rejected by filtering different frequency bands of signals and test statistic was formed after FRFT. Detection performance curves in Gaussian clutter and real sea clutter were obtained by Monte-Carlo simulation, which were close to the matched filter. The results show that the reliable detection of moving target can be achieved in the low signal-to-clutter ratio environment with this method.
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