期刊
ELECTRONICS LETTERS
卷 59, 期 19, 页码 -出版社
WILEY
DOI: 10.1049/ell2.12966
关键词
array signal processing; direction-of-arrival estimation; radar signal processing
A computationally efficient algorithm for angle estimation of bistatic MIMO radar is proposed in this study, which replaces the cumbersome 2D peak searching process with a multimodal quantum-inspired salp swarm algorithm. This algorithm reduces computational complexity, avoids grid errors, and further exploits the potential of the MUSIC algorithm.
In this letter, a computationally efficient multiple signal classification (MUSIC)-based evolutionary algorithm for angle estimation of bistatic multiple-input multiple-output (MIMO) radar is proposed. The existing MUSIC algorithms require a computationally cumbersome two-dimensional (2D) peak searching and the performance is highly related to the grid that set, which leads to a conflict between the computational efficiency and estimation performance. To address this difficulty, a multimodal quantum-inspired salp swarm algorithm, integrating kmeans clustering technique, is proposed to substitute the 2D peak searching to obtain multiple maxima of the MUSIC algorithm. The resulting computationally efficient algorithm obviously reduces the computational complexity of the MUSIC algorithm, avoids grid errors, and further exploits the potential of the MUSIC algorithm. Numerical simulations in various scenarios are carried out to verify the superiority of the method. In this letter, a computationally efficient multimodal quantum-inspired salp swarm algorithm is presented, employing this to form a computationally efficient implementation of the MUSIC algorithm for angle estimation in bistatic MIMO radar. The resulting MUSIC-based algorithm further exploits the potential of the MUSIC algorithm, significantly reduces computational time, avoids grid errors, and overcomes the contradiction between the computational efficiency and estimation performance of the MUSIC algorithm.image
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据