A novel STAP algorithm for airborne MIMO radar based on temporally correlated multiple sparse Bayesian learning
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Publication:1793110
DOI10.1155/2016/3986903zbMath1400.94082OpenAlexW2510706873WikidataQ59131226 ScholiaQ59131226MaRDI QIDQ1793110
Yongshun Zhang, Yiduo Guo, Yifeng Wu, Qiang Wang, Hanwei Liu
Publication date: 12 October 2018
Published in: Mathematical Problems in Engineering (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1155/2016/3986903
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Cites Work
- Direct data domain STAP using sparse representation of clutter spectrum
- Stable recovery of sparse overcomplete representations in the presence of noise
- Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
- Improved M-FOCUSS Algorithm With Overlapping Blocks for Locally Smooth Sparse Signals
- $L_1$-Regularized STAP Algorithms With a Generalized Sidelobe Canceler Architecture for Airborne Radar
- Performance of Two Low-Rank STAP Filters in a Heterogeneous Noise
- Theoretical Results on Sparse Representations of Multiple-Measurement Vectors
- Bayesian Compressive Sampling for Pattern Synthesis With Maximally Sparse Non-Uniform Linear Arrays
- Sparse Bayesian Learning for Basis Selection
- Sparse solutions to linear inverse problems with multiple measurement vectors
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