雷达恒虚警研究1

雷达恒虚警研究 摘要： 本文对雷达CFAR处理方法进行了综述 ,讨论了CFAR检测方法的方向:参量和非参量的 CFAR方法。明确了空域 CFAR 处理的概念，并着重讨论了空域 CFAR 处理研究中 ML类、OS 类和自适应 CFAR 算法。也简单介绍了时域CFAR处理和非参量CFAR处理的方法。并且提到了分布式CFAR检测 ,阵列信号 CFAR处理 ,极化 CFAR处理等极具潜力的研究方向。最后针对几种典型的恒虚警检测算法的性能、优缺点进行了讨论。

关键字： 参量和非参量CFAR 空域CFAR 时域CFAR ML-CFAR OS-CFAR 自适应CFAR性能分析 雷达系统仿真

第Ⅱ页 Title： Method and Principle of Radar signal CFAR

Abstract： This paper reviews on the radar CFAR processing method, the direction of CFAR method for detection: parametric and non-parametric CFAR method. Make a clear concept of the spatial CFAR processing. And discusses the class ML, class OS and adaptive CFAR algorithm of the spatial CFAR. Also simply introduced the time domain CFAR processing method and non-parameteric CFAR processing. And mentioned the distributed CFAR detection, array signal processing of CFAR, research direction of polarization CFAR processing potential. Finally, the performance and advantages and disadvantages of several typical CFAR detection algorithm are discussed.

Keywords： parametric and non-parametric CFAR spatial CFAR time domain CFAR ML-CFAR OS-CFAR adaptive CFAR performance analysis 雷达系统仿真

第Ⅲ页 目次 1 引言 „„„„„„„„„„„„„„„„„„„„„„„„„„ 1 2 恒虚警处理方法的分类„„„„„„„„„„„„„„„„„„„„2 3 均值类（ML）CFAR处理 „„„„„„„„„„„„„„„„„„„ 3 3.1 单元平均（CA-CFAR）检测算法„„„„„„„„„„„„„„„ 3 3.2 最大选择（GO-CFRA）检测算法„„„„„„„„„„„„„„„ 4 3.3 最小选择（SO-CFAR）检测算法„„„„„„„„„„„„„„„ 5 4 有序统计量（OS-CFAR）处理„„„„„„„„„„„„„„„„„ 6 4.1 顺序统计量检测算法„„„„„„„„„„„„„„„„„„„ 6 4.2 删除均值（CMLD-CFAR）有序统计量算法„„„„„„„„„„„ 6 4.3 削减平均（TM-CFAR）有序统计量算法„„„„„„„„„„„„ 7 4.4 其他有序统计量算法„„„„„„„„„„„„„„„„„„„ 7 5 自适应CFRA处理„„„„„„„„„„„„„„„„„„„„„„ 8 6 时域CFAR处理——杂波图CFAR检测„„„„„„„„„„„„„ 9 7 非参量CFAR处理„„„„„„„„„„„„„„„„„„„„„„10 7.1 符号检测器„„„„„„„„„„„„„„„„„„„„„„„ 10 7.2 Wilcox on检测器„„„„„„„„„„„„„„„„„„„„ 10 8 其他CFAR处理的研究„„„„„„„„„„„„„„„„„„„„11 8.1 频域 CFAR检测„„„„„„„„„„„„„„„„„„„„„ 11 8.2 分布式 CFAR检测„„„„„„„„„„„„„„„„„„„„ 11 8.3 阵列信号CFAR检测„„„„„„„„„„„„„„„„„„„„11 8.4 极化CFAR检测„„„„„„„„„„„„„„„„„„„„„„11 8.5 多分层CFAR处理„„„„„„„„„„„„„„„„„„„„„12 9 对均值类及有序统计量类算法的性能分析„„„„„„„„„„„ 13 9.1 均匀杂波背景下的检测性能„„„„„„„„„„„„„„„„ 13 9.2 五种恒虚警方法的ADT„„„„„„„„„„„„„„„„„„„13 9.3 强干扰目标下的检测性能„„„„„„„„„„„„„„„„„ 14 9.4 均值类（ML）的优缺点„„„„„„„„„„„„„„„„„„ 14 9.5 有序统计量类（OS）的优缺点„„„„„„„„„„„„„„„ 15 结论 „„„„„„„„„„„„„„„„„„„„„„„„„„„„ 16 致谢 „„„„„„„„„„„„„„„„„„„„„„„„„„„„ 17 参考文献„„„„„„„„„„„„„„„„„„„„„„„„„„ 18 雷达系统仿真 第1页 1 引言 雷达是军事和民用领域主要的目标探测工具，它的主要目的是在各种干扰存在的杂波背景下检测出有用目标。这些干扰包括接收机内部热噪声、地物、雨雪、海浪等杂波，电子对抗措施，人工有源和无源干扰(如干扰发射机和金属箔条) ，以及与有用目标混杂在一起的邻近干扰目标和它的旁瓣(如采用脉冲压缩的雷达)。一般说来，这些干扰不是单一存在的，实际的雷达工作背景都是多种干扰的混合。如何在极为复杂的杂波背景下准确区分有用目标回波，并得到目标的一些参数，这是雷达目标信号检测的重点和难点所在。 雷达目标自动检测中若采用固定阈值检测，杂波功率的微小增加将会使得虚警率剧烈变化，从而导致雷达数据处理设备过载，雷达无法工作，这时即使信噪比很大也无法做出正确判断。故在对回波信号进行提取时，需要检测器具有恒虚警性能。 恒虚警处理就是一种提供检测阀值的数字信号处理算法，其算法有许多。本文将介绍恒虚警处理的几种方法及其原理，并简述其的适用范围和性能。 雷达系统仿真

第2页 2 恒虚警处理方法的分类 对 CFAR 的研究只是在近三十年才发展起来的。但是现已成为国际雷达信号处理界的一个重要研究方向，并且形成了如下一些研究领域：高斯和非高斯杂波背景中的 CFAR检测；参量和非参量 CFAR 方法；时域和频域的CFAR研究；标量和向量(阵列信号处理) CFAR方法；单传感器和多传感器分布式 CFAR 检测；相关和不相关条件下的CFAR检测；以及在各种目标模型条件下和结合各种检测策略的 CFAR处理的性能分析。这些领域是相互交叉的。 而本文将CFAR分为参量和非参量两大类。参量 CFAR 方法适用于杂波分布类型已知的情况。按照不同的参数估计方法，参量CFAR 方法又可分为空域 CFAR 处理和时域 CFAR处理。非参量 CFAR 方法适用于杂波分布未知的情况，无须关于背景噪声或杂波分布的先验信息。 为了简化对 CFAR 检测的性能分析，Rohling将均匀和非均匀杂波背景简化为 3 种典型情况， 即均匀背景、多目标和杂波边缘环境。根据这三种情况，空域CFAR处理就分为均值类（ML）CFAR处理、有序统计量类（OS）CFAR处理和自适应CFAR处理。参量 CFAR处理中的另一类是时域CFAR处理，即杂波图CFAR处理。 在均值类（ML）CFAR处理中，又有几种经典算法。它们分别是单元平均（CA-）、最大选择（GO-）、最小选择（SO-）和杂波强度加权（WCA –CFAR）检测。而自适应CFAR处理是现在热门研究的方向，人们已研究了许多类型的CFAR处理技术，如CCA、HCE、AC、GCMLD、ACCA等。 非参量CFAR处理中又分为基于符号的检测器和基于秩的检测器。 雷达系统仿真

第3页 3 均值类（ML）CFAR处理 CFAR算法的基本流程如图 1所示。输入信号包括检测单元 Y和 2n个参考单元。参考单元位于检测单元两侧，前后各 n个。保护单元主要用在单目标情况下，防止目标能量泄漏到参考单元影响检测效果。Z为总的杂波功率水平的估计，通过对 2n个参考单元的 CFAR处理得到。T为标称化因子，它和 Z的乘积作为参考门限电平。当检测单元的值超过 T ×Z时，认为有目标，反之，认为无目标。 一般情况下，杂波同噪声相互独立，且平方律检波后都满足指数分布。参考

单元概率密度函数为/21,02xfxex(1) 式中，μ是噪声功率。Z是一个随机变量，它的分布取决于CFAR算法的选取以及参考单元的分布。虚警概率 Pfa的表达式为

0{P[YTZ|H]}E{(1/2)exp(y/2)dy}E{exp(TZ/2)}M(T/2)fazZTZ

ZZ

PE

 (2)

其中，H0表示没有目标，MZ (μ)称为矩母函数。 3.1 单元平均（CA-CFAR）检测算法 在CA – CFAR检测器中，背景杂波功率水平Z为 2n个参考单元之和。 22111nnniiiiiniZXXX

（3）

指数分布是Γ(α,β)分布在α= 1的特殊情况，Γ分布的概率密度函数为1//,0,0,0xfxxex（4）。其中，α和β是两个参数，Γ

(α)就是通常说的Γ函数，对于整数α，它等于 (α- 1) !。相应的概率分布函数用 G (α,β)表示，服从Γ分布的随机变量 X记做 X～G (α,β)。X的矩母函数为1XM(5) 根据独立同分布的假设，第 i个单元服从分布xi～G (1,μ)。由于两个独立随机变量和的矩母函数等于各随机变量的矩母函数的积，所以得到Z ～G (2n,μ) (6)将式 (5)、 式 (6)代入式 (2)得到2[1T]nfaP (7)所以,得到标称化