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XU Zhenyu, LIU Weijian, CHEN Xiaolong, et al. Adaptive detection of range-distributed targets in weighted generalized inverse gaussian clutter[J]. Journal of Radars, in press. doi: 10.12000/JR25072
Citation: XU Zhenyu, LIU Weijian, CHEN Xiaolong, et al. Adaptive detection of range-distributed targets in weighted generalized inverse gaussian clutter[J]. Journal of Radars, in press. doi: 10.12000/JR25072
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Adaptive Detection of Range-distributed Targets in Weighted Generalized Inverse Gaussian Clutter

DOI: 10.12000/JR25072 CSTR: 32380.14.JR25072
  • 1.

    Air Force Early Warning Academy, Wuhan 430019, China

  • 2.

    Naval Aviation University, Yantai 264001, China

Funds:  The National Natural Science Foundation of China (62471485, 62071482, 62222120), National Natural Science Foundation of Hubei Province (2025AFB873)
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  • Corresponding author: LIU Weijian, liuvjian@163.com
  • Received Date: 2025-04-17
  • Rev Recd Date: 2025-07-03
    • Available Online: 2025-07-09
    Abstract
  • In this paper, we investigate the adaptive detection of range-distributed targets in compound-Gaussian clutter, where the texture component follows a Weighted Generalized Inverse Gaussian (WGIG) distribution. We propose adaptive detectors for WGIG-distributed clutter based on two-step Rao, Wald, Durbin, and Gradient tests. The unknown covariance matrix is estimated using Approximate Maximum Likelihood (AML) and the Normalized Sample Covariance Matrix (NSCM). To address the analytical intractability of Maximum A Posteriori (MAP) estimation for the texture component, we adopt an alternative approach: The MAP estimator of the reciprocal expectation of the texture component, which is used in designing adaptive detectors based on the Rao, Wald, and Durbin tests. For the Gradient test-based detector, the test statistic is derived directly from the posterior probability density function. Our theoretical analysis confirms the consistency of the detectors derived from the Rao, Durbin, and Gradient tests. Extensive evaluations on both simulated and real data yield three key findings: (1) the proposed AML-based detectors maintain the constant false alarm rate property; (2) under matched signal conditions, the detectors based on the Rao and Wald tests achieve the best performance on both the IPIX radar dataset and the Journal of Radar’s maritime surveillance dataset—specifically, they outperform the two-step generalized likelihood ratio test-based detector, requiring 0.1～0.5 dB and 0.7～0.8 dB lower Signal-to-Clutter Ratio (SCR) to achieve the same detection probability, respectively; and (3) under mismatched signal conditions, the Rao test-based detector with AML estimation exhibits superior robustness, while the Wald test-based detector demonstrates the strongest suppression capability against mismatched signals.

     

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