空时自适应杂波分类抑制与弱小运动目标检测

SPATIAL-TEMPORAL ADAPTIVE CLUTTER CLASSIFICATION SUPPRESSION AND DIM SMALL MOVING TARGETS DETECTION

提出了一种新的方法应用于一类重要的高维信号检测问题:在强杂波干扰下检测数字图像序列中位置和速度未知的弱小运动目标.通过对输入序列时域灰度矩进行学习,将像素分成两类———静杂波和动杂波.分别对其采用非参数时域滤波和LS自适应滤波进行去除,从而将原始数据转化为准SPGWN模型.杂波抑制后,根据单帧多像素目标模型假设,采用在空、时域联合集成信号能量的检测算法,能有效地改善信噪比并且有利于实时实现.理论分析和对真实数据的大量仿真试验验证了本方法的有效性.

A new method was proposed for the solution of an important class of multidimensional signal detection problems ： the detection of dim, small and moving targets of unknown position and velocity in heavy clutter in a sequence of digital images. By studying temporal gray-level moment of input sequence, the pixels were classified into two categories： stationary clutter and variational clutter. And a nonparametric temporal filter and a LS adaptive filter were applied for suppressing clutter respectively, thus the raw images were transformed into quasi SPGWN model. Then according to a target modei of muhi-pixel per frame, a detection algorithm integrating signal energy in spatial and temporal domain jointly was employed. The algorithm can improve SNR evidently and can easily be implemented in real time. The theoretic analysis and many simulations of real data verify the validity of the method.