Knowledge-aided Bayesian detection with mimo radar in compound-gaussian clutter

Li, N.; Cui, G.; Yang, Haifeng; Kong, Lingjiang; Liu, Q.H.

doi:10.1049/cp.2015.1118

Cited by 2 publications

(3 citation statements)

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“…For mathematical analytical tractability, this paper also adopts the complex inverse Wishart distribution [53] with a stochastic but unknown clutter covariance matrix R, which is inherited by [43], [45], [46], and is given by…”

Section: Problem Formulationmentioning

confidence: 99%

“…Thus, some excellent works [5], [42]- [44] have been proposed to compensate above disadvantages. Furthermore, Li et.al [45] proposed to utilize the Bayesian framework to employ the a-priori information of the covariance matrix, which is assumed to be unknown but stochastic. Therefore, the covariance matrix adopts a probability density the same as the prior knowledge.…”

Section: Introductionmentioning

confidence: 99%

“…As pointed out in [45], [46], a priori information for the covariance matrix can be obtained from some state-of-the-art technologies e.g., geographical information systems (GISs) and SAR images, which show the availability of a Bayesian architecture based target detection. Meanwhile, [33], [43], [45], [46] have studied the Bayesian framework for a target detection by the MIMO radar. However, the higher computational complexity is the major drawback of the above methods that inspires us to present a method for obtaining reduced complexity.…”

Section: Introductionmentioning

confidence: 99%

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Low-complexity Bayesian-GLRT-Based Target Detection For MIMO Radar in Gaussian Clutter

Li,

Yang,

2024

IEEE Access

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This paper proposes an unstructured adaptive moving target detection approach for a multipleinput multiple-output (MIMO) radar to obtain reduced computational complexity. Moreover, the MIMO radar operates in a Gaussian clutter with an unknown but stochastic covariance matrix. At the design stage, the Bayesian unstructured generalized likelihood ratio test (UGLRT), namely the BUGLRT detector, which inherits from the UGLRT approach, has been derived. The BUGLRT approach has transformed 2D searching of structured generalized likelihood ratio test (SGLRT) based on the Bayesian framework, namely BSGLRT, detector into a 1D searching procedure that drastically reduces the computational complexity. The asymptotic probability of false alarms and computation complexity have also been analyzed. Both theoretical analysis and numerical simulation results show that BSGLRT and BUGLRT detectors outperform their non-Bayesian counterparts, especially for a small number of snapshots. Moreover, the detection performance of BUGLRT is better than that of BSGLRT, but the performance gap decreases as the number of snapshots increases. In addition, the detectors based on the Bayesian framework are sensitive to the existing mismatches of parameters for inverse complex Wishart distribution. Compared with BSGLRT, the BUGLRT can obtain a noticeable complexity reduction along with a slightly improved detection performance.INDEX TERMS Bayesian detection, generalized likelihood ratio test (GLRT), mutiple-input mutiple-output (MIMO) radar, maximum a posteriori (MAP), moving target detection, unstructured.

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