Sparse Bayesian learning-based mainlobe blanket jamming suppression algorithm

Abstract

The suppression of mainlobe jamming (MLJ) is a hard task and an open problem in the electronic counter-countermeasures field. In this article, a sparse Bayesian learning (SBL)-based mainlobe blanket jamming suppression algorithm is proposed for solving this problem, which can effectively suppress the MLJ and estimate the target distance and direction. The proposed algorithm can be divided into two steps. First, the row (or column) spatial adaptive processing is adopted to suppress the MLJ; Second, the SBL algorithm is used to obtain the target echo parameters.

Keywords

• electronic countermeasures
• interference suppression
• learning (artificial intelligence)
• bayes methods
• jamming
• radar signal processing
• electronic counter-countermeasures field
• sparse bayesian learning-based mainlobe blanket jamming suppression algorithm
• mlj
• sbl algorithm
• mainlobe jamming
• open problem