基于特征的海面低速小目标检测工程算法

A Practical Method for Low Speed Small Trarget Detection within Sea Clutter Based on Target Feature

为了提高海面雷达对海漂浮小目标检测能力,提出了一种基于目标时频特征的检测算法。首先,验证了从接收的时间序列中提取的相对多普勒峰高、相对多普勒偏移、相对多普勒熵等特征在时间维度上可以有效地区分海杂波和小目标;其次,构造了特征检测器,给定虚警概率下的判决区域由凸包算法确定;最后,根据实测数据对算法进行了检验。结果表明:当虚警概率为0.01时,采用双特征检测器可在512个脉冲下完全区分海杂波和实测小目标,双特征检测算法优于传统动目标检测算法。

In order to enhance the floating detection ability of sea surveillance radar, an algorithm based on the feature extracted from the echo of sea surface is proposed. Firstly, some features such as relative Doppler peak, relative Doppler offset, relative Doppler entropy are extracted from the time series of the echo to verify the efficiency of separating sea clutter and small floating target in time dimension. Secondly, the feature detector is constructed which the decision region under the given false alarm rate is determined by convex hull algorithm. Finally, the efficiency of the detector is tested by real radar data, the result shows that it can fully distinguish cooperative target from sea clutter in 512 pulses when the false alarm rate is 0.01. The performance of double feature detector is better than traditional moving target detection method.