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Adaptive Bayesian Detection for MIMO Radar in Gaussian Clutter
doi: 10.12000/JR18090
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Abstract
For collocated Multiple-Input Multiple-Output (MIMO) radar, we investigate the target detection problem in Gaussian clutter with an unknown but random covariance matrix. An inverse complex Wishart distribution is chosen as prior knowledge for the random covariance matrix. We propose two detectors in the Bayesian framework based on the criteria of the Generalized Likelihood Ratio Test. The two main advantages of the proposed Bayesian detectors are as follows: (1) no training data are required; and (2) a prior knowledge about the clutter is incorporated in the decision rules to achieve detection performance gains. Numerical simulations show that the proposed Bayesian detectors outperform the current commonly used non-Bayesian counterparts, particularly when the sample number of the transmitted waveform is small. In addition, the performance of the proposed detector will decline in parameter mismatched situation. -
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References
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- Figure 1. ROC curves for different K
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Figure 2.
${{{P}}_{\rm{d}}}$ - Figure 3. ROC curves for different L
- Figure 4. ROC curves when one-lag correlation coefficient is mismatched
- Figure 5. ROC curves when L is mismatched