Cited By
View all- Xu XLi SLiang TSun T(2020)Sample selection-based hierarchical extreme learning machineNeurocomputing10.1016/j.neucom.2019.10.013377:C(95-102)Online publication date: 15-Feb-2020
Novel Training Sample Selection Methods for SR-STAP
Pages 154 - 157
Abstract
Space-time adaptive processing based on clutter sparse recovery (SR-STAP) method has attracted considerable attention due to its superiority in severe heterogeneous environments. To ensure good clutter suppression performance, training samples should share similar clutter statistic property with the tested sample. This paper proposes two training sample selection methods for SR-STAP which adopt geometrical distances including Euclidean distance and Riemannian distance to measure the clutter similarity. The interference covariance matrices in these distances are estimated using the sparse recovery theory so as to take the clutter sparse property into consideration. Samples whose distances are smallest are selected as appropriate training samples. Numerical results based on publicly available Mountain Top measured data validate the effectiveness of these proposed methods.
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- Novel Training Sample Selection Methods for SR-STAP
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Published In
November 2016
235 pages
ISBN:9781450347907
DOI:10.1145/3015166
Copyright © 2016 ACM.
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Association for Computing Machinery
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Published: 21 November 2016
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ICSPS 2016
ICSPS 2016: 8th International Conference on Signal Processing Systems
November 21 - 24, 2016
Auckland, New Zealand
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ICSPS 2016 Paper Acceptance Rate 46 of 83 submissions, 55%;
Overall Acceptance Rate 46 of 83 submissions, 55%
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View all- Xu XLi SLiang TSun T(2020)Sample selection-based hierarchical extreme learning machineNeurocomputing10.1016/j.neucom.2019.10.013377:C(95-102)Online publication date: 15-Feb-2020
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