Paper Info
Title
Discrete Interference Suppression Method Based on Robust Sparse Bayesian Learning for STAP.
Abstract
Discrete interference influences the performance of existing space-time adaptive processing methods in practical scenarios. In order to effectively suppress discrete interference in real clutter environment, a discrete interference suppression method based on robust sparse Bayesian learning (SBL) is proposed for airborne phased array radar. In the proposed method, the estimation of spatial-temporal spectrum and the calibration of space-time overcomplete dictionary are carried out iteratively. During one iteration, the prominent components of clutter and discrete interference in the spatial-temporal plane are first estimated by SBL, and then the overcomplete dictionary is calibrated by calculating the error matrix. Because of the robust estimation of spatial-temporal spectral distribution, both the discrete interference and the homogeneous clutter profiles can be effectively suppressed with a small number of space-time data. The effectiveness of the proposed method is verified in the nonhomogeneous environment by utilizing simulated and actual airborne phased array radar data.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2900712
IEEE ACCESS
Keywords
Field
DocType
Discrete interference suppression,nonhomogeneous clutter,sparse Bayesian learning (SBL),STAP
Small number,Bayesian inference,Spectral power distribution,Clutter,Matrix (mathematics),Computer science,Algorithm,Phased array,Interference (wave propagation),Calibration,Distributed computing
Journal
Volume
ISSN
Citations 
7
2169-3536
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Xiaopeng Yang1389.33
Yuze Sun211.04
Jian Yang364.51
Teng Long473.10
Sarkar, T.K.5471117.33