Multi-frame superposition framework for OTFS-based ISAC system: A low complexity parameter estimation approach

Abstract

This work investigates the parameter estimation in integrated sensing and communications (ISAC) systems based on orthogonal time frequency space (OTFS). We first establish an OTFS-based ISAC system for vehicular networks. To overcome the computational complexity limitations in current estimation approaches, a two-dimensional (2D) correlation structure with superimposed multiple frames is established, avoiding multiple iterations and significantly reducing parameter estimation complexity. Building on the 2D correlation structure, an approximate Maximum Likelihood (ML) algorithm based on multi-frame superposition (ML-MFS) is proposed for range and velocity estimation, achieving equivalent estimation performance to conventional methods with substantially lower complexity. To overcome the performance degradation in multi-target scenarios, we develop an estimation method based on the whale optimization algorithm, named WOA-MFS, enabling parallel optimization of all target parameters and overcoming the limitations of block optimization in ML-MFS. Additionally, the Cramér-Rao Lower Bound (CRLB) is derived to theoretically characterize the estimation performance limit of the proposed framework. Numerical results demonstrate that both ML-MFS and WOA-MFS significantly reduce computational complexity compared to the conventional ML algorithm, with WOA-MFS outperforming ML-MFS across diverse parameter settings, demonstrating its robustness and effectiveness in diverse scenarios. Meanwhile, the communication performance simulation validates the sensing-assisted communication capability of the proposed system.