Adaptive Detection and Statistical Performance Analysis in Nonzero Mean Clutter

LIU Weijian; XU Zhenyu; ZHANG Jing; QI Chongying; GE Jianjun; CHEN Hui

doi:10.11999/JEIT250935

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LIU Weijian, XU Zhenyu, ZHANG Jing, QI Chongying, GE Jianjun, CHEN Hui. Adaptive Detection and Statistical Performance Analysis in Nonzero Mean Clutter[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250935

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LIU Weijian, XU Zhenyu, ZHANG Jing, QI Chongying, GE Jianjun, CHEN Hui. Adaptive Detection and Statistical Performance Analysis in Nonzero Mean Clutter[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250935

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Adaptive Detection and Statistical Performance Analysis in Nonzero Mean Clutter

doi: 10.11999/JEIT250935 cstr: 32379.14.JEIT250935

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)

Received Date: 2025-09-19
Accepted Date: 2025-11-12
Rev Recd Date: 2025-11-09

Available Online: 2026-06-15

Abstract

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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References(20)

References

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