Enhanced ISAC Framework for Moving Target Assisted by Beyond-Diagonal RIS: Accurate Localization and Efficient Communication
Dawei Wang, Zijun Wang, Weichao Yang, Hongbo Zhao, Yixin He, Li Li, Zhongxiang Wei, Fuhui Zhou
Abstract
This paper proposes an innovative Integrated Sensing and Communication (ISAC) framework for moving target detection by leveraging beyond-diagonal RIS (BD-RIS) to improve beamforming performance and control wireless propagation. In this framework, we first design a novel target-tracking method for moving target detection based on Extended Kalman Filtering (EKF) with accurate cooperative localization. In addition, to further improve the sensing accuracy, we minimize the joint posterior Cramér-Rao bound (PCRB) for both target position and velocity constrained by the communication performance requirements, and maintain the orthogonality and symmetry constraints of BD-RIS. Given the non-convex nature of the problem, we break it into two subproblems, which are solved iteratively using the proposed alternating optimization (AO) algorithm. The AO algorithm incorporates a semidefinite relaxation (SDR) method for beamforming and a penalty dual decomposition (PDD) approach for BD-RIS optimization. The simulation results demonstrate that: (1) the proposed prediction method accurately tracks the position and velocity of the target in dynamic environments; (2) the proposed AO algorithm is efficient and effective, exhibiting fast convergence and achieving a performance improvement of 6.7 % compared to conventional diagonal RIS.