Airborne Bistatic Radar Clutter Suppression Based on Sparse Bayesian Learning

Xiaode LÜ; Jingmao YANG; Qi YUE; Hanliang ZHANG

doi:10.11999/JEIT180062

Volume 40 Issue 11

Oct. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(11): 2651-2658

Xiaode LÜ, Jingmao YANG, Qi YUE, Hanliang ZHANG. Airborne Bistatic Radar Clutter Suppression Based on Sparse Bayesian Learning[J]. Journal of Electronics & Information Technology, 2018, 40(11): 2651-2658. doi: 10.11999/JEIT180062

Citation:

Xiaode LÜ, Jingmao YANG, Qi YUE, Hanliang ZHANG. Airborne Bistatic Radar Clutter Suppression Based on Sparse Bayesian Learning[J]. Journal of Electronics & Information Technology, 2018, 40(11): 2651-2658. doi: 10.11999/JEIT180062

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Airborne Bistatic Radar Clutter Suppression Based on Sparse Bayesian Learning

doi: 10.11999/JEIT180062

Xiaode LÜ^{1, 2},
Jingmao YANG^{1, 2, 3
,
,},
Qi YUE^{1, 2, 3},
Hanliang ZHANG^{1, 2, 3}

1.
Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
2.
National Key Laboratory of Science and Technology on Microwave Imaging, Beijing 100190, China
3.
University of Chinese Academy of Sciences, Beijing 100049, China

Received Date: 2018-01-16
Rev Recd Date: 2018-08-13

Available Online: 2018-08-22

Publish Date: 2018-11-01

Abstract

Abstract

Clutter of airborne bistatic radar is related to configuration and has serious range dependence characteristic, therefore the clutter ridge is complex and variable, and few Independent and Identically Distributed (IID) samples exist. As the result, the traditional Space-Time Adaptive Processing (STAP) has a degraded suppression performance for airborne bistatic radar clutter. Based on the sparsity of airborne radar clutter in the angle-Doppler domain and the advantages of Sparse Bayesian Learning (SBL) in sparse signal reconstruction, SBL algorithm is applied to the more complex airborne bistatic radar with both transmitter and receiver moving. The method can estimate the Clutter Covariance Matrix (CCM) of the unit under test with very few training samples, then perform space-time adaptive processing. Since the method does not need independent and identically distributed samples, it has better performance of clutter suppression in the airborne bistatic radar with both transmitter and receiver moving. Simulation results verify the effectiveness of the algorithm.
- Clutter suppression,
- Sparse signal reconstruction,
- Space-Time Adaptive Processing (STAP),
- Sparse Bayesian Learning (SBL)

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References(27)

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