OTFS-Assisted ISAC System: Delay Doppler Channel Estimation and SDR-Based Implementation

Orthogonal Time-Frequency Space (OTFS) modulation is an emerging technique that characterizes wireless channels and transmits information in the delay-Doppler domain. This work focuses on estimating fundamental sensing parameters, i.e., the delay and Doppler shifts of individual propagation paths, which serve as critical enablers for downstream positioning techniques, such as time-difference-of-arrival (TDOA)-based localization. Specifically, we propose a parameter-inherited (PI) channel estimation method that integrates sparse Bayesian learning (SBL) with unitary approximate message passing (UAMP), achieving low computational complexity and high estimation robustness. To accelerate the convergence of the UAMP-based iterative estimation, we explore the strategy of initializing parameters by inheriting prior estimates from adjacent OTFS transmission blocks. Furthermore, the overall computational burden is significantly reduced by employing large-scale matrix operations via two-dimensional fast Fourier transform (2D FFT). The proposed algorithms are implemented and evaluated on a software-defined radio (SDR)-based ISAC platform. Experimental results demonstrate that the proposed dual-functional system outperforms existing benchmarks in both communication quality and sensing parameter accuracy.