Open AccessSparse Bayesian learning‐based mainlobe blanket jamming suppression algorithm
Author(s) -
Zhou Bilei,
Duan Keqing,
Liu Weijian,
Li Rongfeng,
Wang Yongliang
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0407
Subject(s) - jamming , computer science , algorithm , bayesian probability , blanket , field (mathematics) , echo (communications protocol) , artificial intelligence , mathematics , physics , thermodynamics , computer network , pure mathematics , history , archaeology
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.