利用相位特征筛选参考单元的改进CFAR方法

Improved CFAR Method Using Phase Feature to Select Reference Unit

在传统单元平均恒虚警的基础上,提出一种利用相位特征筛选参考单元的改进恒虚警方法:通过判断连续脉冲回波在同一参考单元时的相位线性情况,剔除相位线性度强的参考单元,形成待检测单元适应性更好的检测门限,并通过仿真与实测数据测试,对比该算法与其他均值类恒虚警算法在不同环境背景下的检测性能。结果表明:在均匀单目标背景下,改进的恒虚警算法与单元平均恒虚警检测性能相当,比其他均值类检测器检测性能更好;而在多目标环境与杂波边缘环境下,改进的恒虚警算法相比其他均值类恒虚警算法能更好地避免目标遮蔽现象,检测性能更好。

Based on the traditional Cell Average Constant False Alarm Rate(CA-CFAR),an improved Constant False Alarm Rate(CFAR)method using phase features to filter reference cells is proposed.By judging the phase linearity of continuous pulse echo in the same reference unit,the reference unit with strong phase linearity is eliminated,and the detection threshold with better adaptability of the unit to be detected is formed.The detection performance of the algorithm and other mean CFAR algorithms in different environmental backgrounds is compared by simulation and measured data tests.Results show that under the background of uniform single target,the improved CFAR algorithm has the same performance as the CA-CFAR detection,and the detection performance is better than other mean detectors.In the multi-target environment and clutter edge environment,the improved CFAR algorithm can better avoid the target occlusion phenomenon and has better detection performance than other mean constant false algorithms.