Target Parameter Estimation for OTFS-Integrated Radar-Communication Systems

In high-mobility scenarios envisioned for 6G communications, orthogonal time frequency space (OTFS) has emerged as a promising alternative to conventional orthogonal frequency division multiplexing (OFDM) schemes. To address the parameter estimation problem in OTFS-integrated radar and communication systems, we propose a two-stage parameter estimation framework based on sparse reconstruction and the Alternating Direction Method of Multipliers-Newton (ADMM-Newton), which exploits the sparsity of OTFS signals in the delay-Doppler (DD) domain to enable precise parameter estimation in high-mobility scenarios. In the first step, we construct an echo signal model for an OTFS-integrated radar-communication system and estimate the delay and Doppler integer grid values of the detected targets using a sparse reconstruction-based ADMM algorithm. In the second step, the estimates of the integer grid parameters are used as constraints for a more specific search. Fractional-precision estimations of the range and velocity parameters are obtained by iteratively solving for the Newton direction via the Newton algorithm. Finally, numerical simulations are executed to verify the proposed algorithm’s performance in parameter estimation in integrated sensing and communication (ISAC) systems. The results demonstrate that the proposed algorithm achieves millimeter-level accuracy in velocity estimation under single-target simulation scenarios. Particularly in situations involving multiple sensing targets, the proposed method can estimate target range and velocity with centimeter-level accuracy. The proposed algorithm effectively eliminates notable differences between estimated and real values due to three targets interfering with one another, demonstrating strong estimation accuracy. Furthermore, in target conditions that are closely spaced, the proposed algorithm maintains robust estimation accuracy.