海面漂浮小目标的特征联合检测算法

该文研究了高距离分辨海杂波背景下漂浮小目标的检测问题。漂浮目标使得周围海面的散射特性发生了改变,目标所在的分辨单元的回波满足非加性模型,导致该模型中依赖于目标的参数难以统计建模。为了避开参数建模,该文将检测问题转化为二元分类问题,即确定海杂波所属于的类,目标检测就是判别回波是否属于该类。针对此分类问题,提出了基于非加性模型的特征联合检测算法,首先在回波中提取两个特征组成归一化向量,然后利用凸包训练算法获得判别区域,最后以判别区域是否包含该向量作为判别准则。实测的IPIX雷达数据实验结果表明,该文算法在高分辨海杂波下的检测性能优于对比算法,为海事雷达检测小目标提供了新的检测方案。

Feature United Detection Algorithm on Floating Small Target of Sea Surface

This paper focus on the detection of floating small targets in high range resolution sea clutter.Floationg targets disarrange the scattering of neighboring sea surface,which results in that the received echoes in the cell targets located satisfy a non-additive model.While,it is hardly to model the paramters correlated to targets in the non-additive model.In order to keep away from the parameter modeling,target detection can be regarded as a binary-classification,where the clutter-only pattern is available for the classifier design and target detection is to judge whether the received echoes belong to the clutter-only pattern.For the classification,a feature united detection algrithm based on the non-additive model is proposed in the paper.First,two extracted features from the received echoes are combined into a normalized vector for target detection.Then,a convex hull training algorithm is utilized to determine a decision region.Finally,the detection rule is whether the decision region surrounds the vector.Experimental results by the raw IPIX radar data show that the proposed algorithm outperforms the compared algorithms.It provides a new detection guidance for the marine radar to detect samll targets.