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Persymmetric adaptive detection of range-spread targets in subspace interference plus Gaussian clutter

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Abstract

In this paper, we consider the adaptive detection problem of range-spread targets embedded in subspace interference plus structured Gaussian clutter. The target signal and interference are assumed to lie in two linearly independent subspaces with unknown coordinates. The clutter component is modeled as a complex Gaussian vector with an unknown persymmetric covariance matrix. We leverage the persymmetric structure to design a two-step detector according to the Rao test criterion. The theoretical results show that the proposed detector possesses the constant false alarm rate property with respect to the clutter covariance matrix. Furthermore, the numerical results show that the proposed detector exhibits better detection performance than the existing unstructured subspace detectors, particularly under a limited training data size. In addition, the proposed detector outperforms the existing persymmetric subspace detectors.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61971432, 61790551), Taishan Scholar Project of Shandong Province (Grant No. tsqn201909156), Outstanding Youth Innovation Team Program of University in Shandong Province (Grant No. 2019KJN031), and Technical Areas Foundation for Fundamental Strengthening Program (Grant No. 2019-JCJQ-JJ-060). The authors would like to thank the anonymous reviewers for their valuable comments and suggestions that helped to greatly improve the quality of the paper.

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Authors and Affiliations

  1. Research Institute of Information Fusion, Naval Aviation University, Yantai, 264001, China

    Tao Jian, Jia He, Yu Liu, You He, Congan Xu & Zikeng Xie

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  1. Tao Jian
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  2. Jia He
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  3. Yu Liu
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  4. You He
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  5. Congan Xu
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  6. Zikeng Xie
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Correspondence to Tao Jian.

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Jian, T., He, J., Liu, Y. et al. Persymmetric adaptive detection of range-spread targets in subspace interference plus Gaussian clutter. Sci. China Inf. Sci. 66, 152306 (2023). https://doi.org/10.1007/s11432-022-3540-5

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  • DOI: https://doi.org/10.1007/s11432-022-3540-5

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