多目标环境下跑道异物监测雷达的CFAR检测技术

CFAR Detection Method in Multiple Target Environments for Foreign Object Debris Surveillance Radar

强地杂波下的弱固定目标检测是机场跑道异物监测雷达需解决的关键问题之一,传统的恒虚警检测算法在多目标环境下会出现"自屏蔽"效应,不能有效地检测到目标。本文提出了一种剔除平均杂波图恒虚警检测算法,在分析系统特性和跑道环境的基础上,结合调频连续波雷达体制建立了多目标回波信号模型。在剔除若干个样本值后,由余下的样本值估计背景杂波强度,对多次连续扫描测量值作递归滤波处理,获得了平稳的检测门限,有效地检测到了目标。最后,进一步讨论了相关参数对检测性能的影响。

Detection of weak targets in heavy ground clutter is one of key issues for Foreign Object Debris （FOD） surveillance radar on runways. Because of ＂Shelf-shielding＂ effect, traditional CFAR detection algorithms cannot effectively detect targets in multiple target environments. A method of trimmed-mean clutter-map CFAR detection is proposed. First, multiple targets echo model of Frequency Modulation Continue Wave （FMCW） radar based on the scenarios of airport runways and characteristics of radar system is established. Then, the largest samples in referencing cells are removed and mean of remaining samples is the clutter-level estimation. To obtain a stable detection threshold to detect targets, the clutter-map cells are updated scan-by-scan. At last, further analysis of main factors which affects detection performance of this algorithm is studied.