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28 March 2024 Adaptive Bayesian detection for multistatic radar in compound-Gaussian clutter with inverse Gaussian texture
Hao Zhou, Shuwen Xu, Yaosheng Cui
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Proceedings Volume 13091, Fifteenth International Conference on Signal Processing Systems (ICSPS 2023); 1309117 (2024) https://doi.org/10.1117/12.3023150
Event: Fifteenth International Conference on Signal Processing Systems (ICSPS 2023), 2023, Xi’an, China
Abstract
This work addresses the problem of adaptive target detection in compound-Gaussian clutter for multistatic radar. We exploit a-priori knowledge of the clutter to alleviate the performance degradation due to the non-Gaussian characteristic of the clutter. Here, the inverse Gaussian distribution is adopted to describe the texture of the clutter. In addition, the speckle covariance matrix of the clutter is modeled as a random matrix following the complex inverse Wishart distribution. Using different fusion rules between the a-priori knowledge of the texture and the speckle component within the Bayesian framework, two adaptive detectors are derived based on the Generalized Likelihood Ratio Test (GLRT) criterion. Finally, the detection performance of the proposed detectors is verified using simulated data. The results show the superiority of the proposed adaptive detectors over the existing techniques.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Hao Zhou, Shuwen Xu, and Yaosheng Cui "Adaptive Bayesian detection for multistatic radar in compound-Gaussian clutter with inverse Gaussian texture", Proc. SPIE 13091, Fifteenth International Conference on Signal Processing Systems (ICSPS 2023), 1309117 (28 March 2024); https://doi.org/10.1117/12.3023150
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KEYWORDS
Clutter
Radar sensor technology
Radar
Covariance matrices
Multiple input multiple output
Correlation coefficients
Target detection
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