Adaptive Bayesian Detection for MIMO Radar in Gaussian Clutter

HAN Jinwang; ZHANG Zijing; LIU Jun; ZHAO Yongbo

doi:10.12000/JR18090

Volume 8 Issue 4

Aug. 2019

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Article Navigation > Journal of Radars > 2019 > 8(4): 501-509

XIA Deping, ZHANG Liang, WU Tao, et al. A multiple interference suppression algorithm based on airborne bistatic polarization radar[J]. Journal of Radars, 2022, 11(3): 399–407. doi: 10.12000/JR21212

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HAN Jinwang, ZHANG Zijing, LIU Jun, et al. Adaptive Bayesian detection for MIMO radar in Gaussian clutter[J]. Journal of Radars, 2019, 8(4): 501–509. doi: 10.12000/JR18090

Citation:

HAN Jinwang, ZHANG Zijing, LIU Jun, et al. Adaptive Bayesian detection for MIMO radar in Gaussian clutter[J]. Journal of Radars, 2019, 8(4): 501–509. doi: 10.12000/JR18090

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Adaptive Bayesian Detection for MIMO Radar in Gaussian Clutter

DOI: 10.12000/JR18090

HAN Jinwang^1
,,
ZHANG Zijing^1
,,
LIU Jun^{2
,
,},
ZHAO Yongbo^1
,

①.
National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
②.
Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, China

Funds: The National Natural Science Foundation of China (61871469, 61571349), The Natural Science Foundation of Shaanxi Province (2018JM6051)

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Corresponding author: LIU Jun, junliu@ustc.edu.cn
Received Date: 2018-10-25
Rev Recd Date: 2019-01-03
Publish Date: 2019-08-28

Abstract

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.
- Multiple-Input Multiple-Output (MIMO) radar,
- Adaptive detection,
- Bayesian,
- Inverse complex Wishart distribution,
- Generalized Likelihood Ratio Test (GLRT)

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References(34)

References

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