MUSIC-based steering vector used in adaptive nulling suppression
Chen Li-wen, Jian-Sheng Zheng · 2014
This study presents a performance of the linearly-constrained-minimum-variance-criterion (LCMV) based on adaptive nulling technique, but the algorithm will form a large ribbon around the nulling in the direction of the radio frequency spectrum of the interfering signal and is affected by the flow pattern of the antenna array. To solve this problem, Multiple Signal Classification (MUSIC) based on steering vector algorithm is proposed through a two-dimensional spatial spectrum peak search method to get the interference signal jamming model under minimum power conditions. This new model can be used at low signal to noise ratio conditions, resulting in higher resolution and depth deeper nulling, and there are no excess nullings around the spectral interference direction. In this paper, the efficiency between MUSIC algorithm and LCMV bound algorithms are discussed.