MUSIC-Based Moving Target Estimation in RIS-aided ISAC Systems under NLoS Scenarios
Fan Yang, Peichang Zhang, Peng Yi, Junjie Ye, Zhen Chen, Lei Huang · 2024
Reconfigurable intelligent surface (RIS) and integrated sensing and communication (ISAC) technologies are emerging as promising elements in the context of 6th generation networks. However, conventional methods for moving target estimation have limitations including low-resolution and high-complexity estimation. This paper proposes a system that integrates RIS with ISAC, tailored for non-line-of-sight (NLoS) scenarios. A moving target estimation method based on the multiple signal classification (MUSIC) algorithm is introduced to enhance the accuracy of distance and velocity estimation. Additionally, a hierarchical codebook was proposed to estimate the target's direction angle while orthogonal frequency-division multiplexing (OFDM) is employed to spectral efficiency. Simulation results validate the effectiveness of the proposed approach and demonstrate its capability to accurately estimate target distance and velocity.