摘要
在雷达自动检测系统中,通常是将自动检测和恒虚警(CFAR)技术结合使用以保持在变化的杂波环境中获得可预测的检测性能和恒定虚警率。将无偏最小方差估计(UMVE)方法和单元平均(CA)方法结合,提出了一种新的恒虚警检测器(MUMCA-CFAR)。采用UMVE和CA方法产生两个局部估计,再取二者的平均值作为背景噪声功率水平估计。在Swer-lingII型目标假设下,推导出了MUMCA-CFAR在均匀背景下虚警概率和检测概率及多目标环境下检测概率的解析表达式,并与其它方法作了比较,结果表明该检测器在均匀背景和多目标环境下均具有相当优越的检测性能。
The technique of combining auto-detection with CFAR is frequently used in radar system,to get a predictable target detection probability at some false alarm rate cost in time-varying clutter situations.A new CFAR detector（MUMCA-CFAR） based on unbiased minimum-variance estimation and cell averaging is presented in this paper.It takes the sum of UMVE of leading window and CA estimation of lagging window as a global noise power estimation.Under SwerlingII assumption,the analytic expressions of false alarm probability and detection probability in homogeneous background are derived,and the analytic expression of detection probability in multiple target situations is also derived.In contrast to other detectors,the MOSUM-CFAR detector has fairly well detection performance in both homogeneous background and multiple target situations.
出处
《计算机仿真》
CSCD
2008年第9期321-323,共3页
Computer Simulation
基金
国防十一五预研基金资助课题(1010602010401)
关键词
检测
恒虚警率
无偏最小方差估计
单元平均
Detection
Constant false alarm rate（CFAR）
Unbiased minimum-variance estimation（UMVE）
Cell averaging（CA）
