Computationally efficient angle estimation of bistatic MIMO radar based on multimodal optimization

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

Automated summary

In this paper, a computationally efficient multimodal quantum-inspired salp swarm algorithm is proposed and applied to the MUSIC algorithm for angle estimation in bistatic MIMO radar. The resulting algorithm significantly reduces computational time, avoids grid errors, and overcomes the contradiction between the computational efficiency and estimation performance of the MUSIC algorithm.