非零均值杂波中的自适应检测及统计性能分析

刘维建; 徐震宇; 张晶; 齐崇英; 葛建军; 陈辉

doi:10.11999/JEIT250935

非零均值杂波中的自适应检测及统计性能分析

doi: 10.11999/JEIT250935 cstr: 32379.14.JEIT250935

刘维建^1,,
徐震宇^1, ,,
张晶²,
齐崇英³,
葛建军⁴,
陈辉¹

1.
空军预警学院武汉 430019
2.
武汉工程大学计算机科学与工程学院武汉 430205
3.
93209部队北京 100085
4.
中国电子科技集团公司信息科学研究院北京 100144

基金项目: 国家自然科学基金(62471485, 62071482)，湖北省自然科学基金(2025AFB873)

详细信息

作者简介:
刘维建：男，副教授，博士，研究方向为多通道信号检测

徐震宇：男，硕士生，研究方向为多通道信号检测

张晶：女，副教授，博士生，研究方向为多通道信号检测

齐崇英：男，高级工程师，博士，研究方向为多源信息融合、多传感器协同探测

葛建军：男，电科集团首席科学家，博士，研究方向为雷达探测技术、认知与智能技术等

陈辉：男，教授，博士，研究方向为阵列信号处理、雷达抗干扰技术和新体制雷达

通讯作者:
徐震宇　xzy1935620989@163.com

中图分类号: TN95
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- HTML全文浏览量: 22
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- 被引次数: 0
出版历程
- 收稿日期: 2025-09-19
- 修回日期: 2025-11-09
- 录用日期: 2025-11-12
- 网络出版日期: 2026-06-15

Adaptive Detection and Statistical Performance Analysis in Nonzero Mean Clutter

1.
Air Force Early Warning Academy, Wuhan 430019, China
2.
School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, China
3.
Unit 93209, Beijing 100085, China
4.
Information Science Academy, China Electronics Technology Group Corporation, Beijing 100144, China

Funds: The National Natural Science Foundation of China (62471485, 62071482), Natural Science Foundation of Hubei Province (2025AFB873)

摘要

摘要: 针对非零均值杂波中的目标检测问题，该文基于广义似然比(GLRT)、Rao和Wald准则，设计出3种自适应检测器。在统计性能分析阶段，考虑了一种更加广泛的情形，即存在信号失配，该情形指的是待检测数据中的信号分量与系统假定值不一致，得到3种检测器在信号失配时解析的检测概率和虚警概率计算表达式。性能分析表明，这些检测器均具备恒虚警(CFAR)特性，非零均值的幅度特性不会对检测器的检测性能产生影响，而是通过两个物理量产生作用，即输出信杂比(SCR)的损失因子和检测器统计分布的自由度。仿真实验、理论解析结果和实测数据不仅验证了所提方法的有效性，还体现出其相较于常规检测器的优越性。
- 非零均值 /
- 自适应检测 /
- 恒虚警 /
- 统计性能
Abstract: Objective Target detection in nonzero-mean clutter is a central problem in radar and hyperspectral imaging systems. Conventional detectors that assume zero-mean clutter show reduced effectiveness when clutter exhibits nonzero-mean characteristics caused by environmental conditions or interference. The objective of this work is to design adaptive detectors that remain robust in nonzero-mean clutter and to analyze their statistical performance under signal-mismatch conditions. Methods Three adaptive detectors are derived based on the Generalized Likelihood Ratio Test (GLRT), the Rao test, and the Wald test. The detectors are formulated to accommodate unknown clutter mean and covariance matrix by estimating these quantities from training samples. A generalized signal-mismatch scenario is examined, in which the actual signal steering vector may deviate from the nominal vector. Analytical expressions for the Probability of Detection (PD) and False Alarm (PFA) are obtained for each detector to assess statistical performance. Results and Discussions Analytical expressions for the probabilities of detection and false alarm of the three detectors are validated through Monte Carlo simulation. All detectors maintain the Constant False Alarm Rate (CFAR) property. The amplitude characteristic of nonzero mean does not directly affect detection performance; rather, it exerts an effect through the loss factor of the output Signal-to-Clutter Ratio (SCR) and the Degrees Of Freedom (DOFs) of the detectors’ statistical distributions. Numerical evaluations using simulated and real data show that the proposed detectors achieve better performance than conventional approaches. Conclusions The three CFAR adaptive detectors based on the GLRT, the Rao test, and the Wald test are effective for target detection in nonzero-mean clutter. The nonzero mean of clutter affects detection performance in two ways: it reduces the optimal output signal-to-clutter ratio of the detectors and decreases the DOF of their statistical distributions. Simulated data show that, when there is no signal mismatch, the GLRT-NMC detector provides the highest PD. When measured data are used and no signal mismatch is present, either the Rao-NMC or Wald-NMC achieves a higher PD than the GLRT-NMC. Under signal-mismatch conditions, for both measured and simulated data, the Rao-NMC demonstrates the best sensitivity to mismatch, whereas the Wald-NMC exhibits the strongest robustness.
- Nonzero mean /
- Adaptive detection /
- Constant False Alarm Rate (CFAR) /
- Statistical performance

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图 1 仿真数据集下检测器在不同SCR时的检测概率

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图 2 仿真数据集下检测器在不同$ \xi $时的检测性能

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图 3 仿真数据集下检测器在不同$ {\cos ^2}\phi $时的检测概率

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图 4 仿真数据集下检测器在不同$ {\cos ^2}\theta $时的检测概率

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图 5 数据20221112175025_stare_VV的第14个距离单元数据高斯化及标准化和仅标准化拟合瑞利分布结果

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图 6 数据UHF_08_SS2_HH_BW2.5M_PRF1000第14个距离单元数据标准化拟合瑞利分布结果

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图 7 实测数据集下检测器在不同SCR时的检测概率

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图 8 实测数据集下检测器在不同$ {\cos ^2}\theta $时的检测概率

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