Small target embedded in sea clutter has very low echo energy,while its Doppler shift is extremely closed to or overlapping the clutter region.A new algorithm based on joint features is proposed to improve the detection performance.Firstly,the echoes of radar are preprocessed by the technique of median operator-based clutter suppression.Secondly,peak power,kurtosis and entropy are extracted from the test samples to form feature vectors,while training data from the residual sea clutter is utilized to determine a decision region by convex hull algorithm.Finally,the preliminary detection rule is whether the decision region surrounds the test vector,and false alarm trace points are excluded by CFAR threshold according to the distribution characteristics of mixed products of the convex hull.Processing of the measured data from microwave Doppler radar shows that the detection results are consistent with the fact.Furthermore,the detection performance of the proposed method is analyzed by the way of Monte Carlo simulation.It demonstrates that the detector based on joint features outperforms the compared algorithms.It provides a new guidance for the marine radars to detect small targets.%针对海杂波区小目标回波能量较弱,多普勒域与海杂波接近甚至重叠的特点,采用特征联合检测算法提高目标检测性能.首先,对雷达回波进行中位数自适应杂波抑制预处理;然后,对待检测单元提取多普勒峰值、峭度和熵值特征,形成特征向量,对残余杂波训练单元采用凸包算法获得判别区域;最后,以判别区域是否包括待检测特征向量对应点初步判断目标有无,再根据凸包混合积分布设定恒虚警门限滤除虚警.实测微波多普勒雷达数据处理结果表明,目标检测结果和实际情况一致.采用蒙特卡洛仿真对算法性能进行分析,表明该算法的检测性能优于对比算法,为雷达海杂波区目标检测提供了新方案.
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