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<p>This paper has studied the challenge of detecting a range-spread target in interference and noise when the number of training data is limited. The interference is located within a certain subspace with an unknown coordinate, while the noise follows a Gaussian distribution with an unknown covariance matrix. We concentrate on scenarios where the training data are limited and employ a Bayesian framework to find a solution. Specifically, the covariance matrix is assumed to follow an inverse Wishart distribution. Then, we introduce a Bayesian detector according to the Rao test, which has superior detection performance compared to existing detectors in certain situations, as demonstrated by both simulation experiment and real data.</p>

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Bayesian Rao test for distributed target detection in interference and noise with limited training data

  • 期刊论文

  • 发表日期：

    2025年9月26日

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  • Daipeng Xiao,
  • Weijian Liu,
  • Jun Liu,
  • Yuntao Wu,
  • Qinglei Du,
  • Xiaoqiang Hua

摘要

This paper has studied the challenge of detecting a range-spread target in interference and noise when the number of training data is limited. The interference is located within a certain subspace with an unknown coordinate, while the noise follows a Gaussian distribution with an unknown covariance matrix. We concentrate on scenarios where the training data are limited and employ a Bayesian framework to find a solution. Specifically, the covariance matrix is assumed to follow an inverse Wishart distribution. Then, we introduce a Bayesian detector according to the Rao test, which has superior detection performance compared to existing detectors in certain situations, as demonstrated by both simulation experiment and real data.