基于二次统计CFAR处理的目标径向尺寸估计

Target length estimation based on quadratic statistical CFAR processing

利用雷达高分辨距离像(high resolution range profile,HRRP)实现对目标径向尺寸估计,可为目标分类识别提供重要特征判据.实际中常采用双向恒虚警(constant false alarm rate,CFAR)门限法进行目标信号支撑区和噪声区的鉴别,低信噪比条件下现有方法尺寸估计精度较低,且当目标支撑区边缘信号较弱时容易漏检.为解决上述问题,本文通过低门限多次检测对目标-噪声边界进行判决,搜索到目标-噪声边界后通过边缘优化进一步提高尺寸估计精度.实验验证和对比分析表明,本文方法平均估计误差小于10%,明显低于现有CFAR门限尺寸估计方法,且能有效避免边缘弱点漏检以及“野值”干扰,提高强噪情况下目标尺寸估计的稳健性.

Estimating the radial size of targets using radar high resolution range profile(HRRP)can provide important feature criteria for target classification and recognition.In practice,the bi-directional constant false alarm rate(CFAR)threshold method is often used to discriminate the signal support area and noise area of targets.Under the condition of low signal-to-noise ratio,the size estimation accuracy of existing methods is low,and it is easy to miss detection when the edge signal of target support area is weak.In order to solve the above problems,this paper judges the target-noise boundary by multiple detections with low threshold,and further improves the size estimation accuracy by edge optimization after searching the target-noise boundary.Experimental verification and comparative analysis show that the average estimation error of this method is less than 10%,which is significantly lower than the existing CFAR threshold size estimation method,and can effectively avoid missing detection of edge weaknesses and strong noise interference.