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: 2025-10-15

Available Online: 2025-11-18

Abstract

Abstract

Objective Target detection in nonzero-mean clutter is a critical challenge in radar and hyperspectral imaging systems. Traditional detectors assuming zero-mean clutter often suffer performance degradation in practical scenarios where clutter exhibits nonzero-mean characteristics due to environmental factors or interference. This work aims to design adaptive detectors robust to nonzero-mean clutter and analyze their statistical performance under signal mismatch conditions. Methods Three adaptive detectors are derived based on the generalized likelihood ratio test (GLRT), Rao and Wald tests. The detectors are designed to account for unknown clutter mean and covariance matrix, using training samples for estimation. A generalized signal mismatch scenario is considered, where the actual signal steering vector may deviate from the nominal one. Analytical expressions for probability of detection (PD) and false alarm (PFA) are derived for each detector to evaluate performance. Results and Discussions Analytic expressions for the PDs and PFAs of the three detectors are confirmed with Monte Carlo simulation. All the detectors possess the constant false alarm rate (CFAR) property. The amplitude characteristic of nonzero mean does not directly affect the detection performance. Instead, it influences through the loss factor of the output signal-to-clutter ratio (SCR) and the degrees of freedom (DOFs) of the detectors’ statistical distribution. Numerical results based on simulated and real data show that the proposed detectors outperform conventional ones. Conclusions The proposed three CFAR adaptive detectors based on GLRT, Rao and Wald tests are effective for target detection in nonzero-mean clutter. The nonzero mean of clutter affects the detection performance in two aspects: reducing the optimal output SCR of the detectors and decreasing the DOFs of the detector’s statistical distribution. Based on simulated data, when there is no signal mismatch, the GLRT-NMC detector has the highest PD. When using measured data and there is no signal mismatch, either the Rao-NMC or Wald-NMC will provide a higher PD than the GLRT-NMC. When there is signal mismatch, whether with measured data or simulated data, the Rao-NMC has the best mismatch sensitivity, while the Wald-NMC has the best robustness.
- nonzero mean,
- adaptive detection,
- constant false alarm rate (CFAR),
- statistical performance

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References(20)

References

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