Clutter suppression algorithm based on fast converging sparse Bayesian learning for airborne radar
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- 作者：Zetao Wanga ; wangzetao12@126.com" class="auth_mail" title="E-mail the corresponding author ; Wenchong Xieb ; xwch1978@aliyun.com" class="auth_mail" title="E-mail the corresponding author ; Keqing Duanb ; duankeqing@aliyun.com" class="auth_mail" title="E-mail the corresponding author ; Yongliang Wangb ; ylwangkjld@163.com" class="auth_mail" title="E-mail the corresponding author
- 关键词：Space-time adaptive processing ; Clutter suppression ; Sparse representation ; Bayesian inference
- 刊名：Signal Processing
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：130
- 期：Complete
- 页码：159-168
- 全文大小：1894 K
文摘
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We study the problem of clutter suppression in STAP with finite training samples.
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Fast converging sparse Bayesian learning approaches are derived.
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A novel STAP algorithm named as M-FCSBL-STAP is proposed.
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The M-FCSBL-STAP has superior performance in low training support situation.