Effective multiparameter estimation in SIMO-OTFS system for integrated sensing and communication via tensor analysis

In this paper, we investigate the parameter estimation problem in single-input multiple-output orthogonal time-frequency space (SIMO-OTFS) systems for integrated sensing and communication (ISAC), where both the sensing receiver (SR) and the communication receiver (CR) are equipped with multiple antennas and employ analog beamforming (AB) for precoding. To fully exploit the sparsity of the multi-dimensional time-varying channels, we first construct the received signals at the SR and CR as third-order PARAFAC tensor models, respectively. Then, we propose a three-stage algorithm for sensing and channel parameter estimation. Furthermore, the problem of estimating the described parameters is analyzed through the derivation of the Cramér-Rao bound (CRB). Simulation results demonstrate that the proposed algorithm significantly outperforms existing competitive algorithms in both sensing and communication performance, especially in high-dynamic scenarios.