基于排序方差的非参量自动删除检测算法

Nonparametric Automatic Censored Detection Algorithm Based on Ordered Data Variability

为了增强广义符号(GS)检测算法在多目标环境中的检测性能,基于自动删除单元平均(AC-CA)检测算法和GS统计量提出了一种新的非参量检测算法(ACGS),它的前沿和后沿均采用ACCA检测算法进行参考单元选择,然后与检测单元进行比较形成检验统计量。分别针对均匀背景和多目标环境分析了ACGS检测算法的性能,并与GS检测算法进行了比较,最后利用实测数据进行验证。结果表明在均匀杂波背景下,ACGS检测算法较GS检测算法有较小的CFAR损失,但在多目标背景下较GS检测算法取得了较大的性能改善。

In order to improve the detection performance of GS detection algorithm in multiple-target environment, a new nonparametric detection algorithm（ACGS） based on automatic censoring cell averaging （ACCA） and GS is presented. It takes the sum of two ACCA local estimations as the choice of reference cell, then sums the ranks of pulses of the test cells of all sweeps. The performance of the detection algorithm is analyzed in homogeneous environment and multiple-target environment and proved by real sea clutter data. In homogeneous environment, the ACGS detection algorithm has a low CFAR loss compared with the GS detection algorithm. But in the multiple-target environment, it has great performance improvement.