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Clutter suppression algorithm based on fast converging sparse Bayesian learning for airborne radar
- Source :
- Signal Processing. 130:159-168
- Publication Year :
- 2017
- Publisher :
- Elsevier BV, 2017.
-
Abstract
- Adapting the space-time adaptive processing (STAP) filter with finite number of secondary data is of particular interest for airborne phased-array radar clutter suppression. Sparse representation (SR) technique has been introduced into the STAP framework for the benefit of drastically reduced training requirement. However, most SR algorithms need the fine tuning of one or more user parameters, which affect the final results significantly. Sparse Bayesian learning (SBL) and multiple sparse Bayesian learning (M-SBL) are robust and user parameter free approaches, but they converge quite slowly. To remedy this limitation, a fast converging SBL (FCSBL) approach is proposed based on Bayesian inference along with a simple approximation term, then, it is extended to the multiple measurement vector case, and the resulting approach is termed as M-FCSBL. To improve the performance of STAP in finite secondary data situation, the M-FCSBL is utilized to estimate the clutter plus noise covariance matrix (CCM) from a limited number of secondary data, and then the resulting CCM is adopted to devise the STAP filter and suppress the clutter. Numerical experiments with both simulated and Mountain-Top data are carried out. It is shown that the proposed algorithm has superior clutter suppression performance in finite secondary data situation. We study the problem of clutter suppression in STAP with finite training samples.Fast converging sparse Bayesian learning approaches are derived.A novel STAP algorithm named as M-FCSBL-STAP is proposed.The M-FCSBL-STAP has superior performance in low training support situation.
- Subjects :
- Computer science
0211 other engineering and technologies
02 engineering and technology
Bayesian inference
law.invention
law
0202 electrical engineering, electronic engineering, information engineering
Electrical and Electronic Engineering
Radar
021101 geological & geomatics engineering
Covariance matrix
business.industry
020206 networking & telecommunications
Pattern recognition
Sparse approximation
Filter (signal processing)
Space-time adaptive processing
Noise
Control and Systems Engineering
Signal Processing
Clutter
Computer Vision and Pattern Recognition
Artificial intelligence
business
Algorithm
Software
Subjects
Details
- ISSN :
- 01651684
- Volume :
- 130
- Database :
- OpenAIRE
- Journal :
- Signal Processing
- Accession number :
- edsair.doi...........24c355fec0a57d09d1676f4c04f4b7b3