基于检测前识别的MTS-CFAR检测方法

在复杂的强地杂波中检测静止目标时极易产生漏检，因此一直是检测领域中的难题。在双参数恒虚警(DP-CFAR)检测方法的基础上提出了基于检测前识别的多时间尺度恒虚警(MTS-CFAR)检测方法。在检测前基于各类型目标在多时间尺度统计结果上的不同，区分和识别运动目标、静止目标和杂波，减少杂波统计量和目标统计量的干扰，提高对静止目标的检测性能。以交通雷达应用为背景，通过仿真和实测数据定量分析和对比了MTS-CFAR、DP-CFAR、截断统计量恒虚警、改进型单元平均恒虚警等检测算法的性能，证明了本文算法对于静止目标检测效果的优越性和工程应用价值。

A method of Multi-Time-Scale Constant False Alarm Rate detection based on pre-detection identification

The miss detection is liable to occur when detecting stationary targets in complex strong ground clutter. Based on the Double-Parameter-Constant-False-Alarm-Rate(DP-CFAR) detection method, this paper proposes a Multi-Time-Scale Constant False Alarm Rate(MTS-CFAR) detection based on pre-detection recognition. Before the detection, based on the difference in the multi-time-scale statistical results of various types of targets, the moving targets, stationary targets and clutter are distinguished and identified, the interferences of clutter statistics and target statistics are reduced, and the detection performance of stationary targets is improved. Based on the application of traffic radar, the performance of MTS-CFAR algorithm and DP-CFAR, TS-CFAR and CAG-CFAR are analyzed and compared quantitatively through simulation and measured data, which proves the superiority and engineering application value of static target detection of the proposed algorithm.