Optimally Deployed Multistatic OTFS-ISAC Design With Kalman-Based Tracking of Targets

TL;DR

This paper introduces an optimized multistatic OTFS-ISAC system with Kalman filtering for accurate target tracking and localization in vehicular networks, improving sensing accuracy and communication reliability.

Contribution

It proposes a triangulation-based deployment framework with a suboptimal receiver placement strategy for enhanced target localization in multistatic OTFS-ISAC systems.

Findings

Significant reduction in sensing error.

Improved bit error rate performance.

Effective target localization and velocity estimation.

Abstract

This paper studies orthogonal time-frequency space (OTFS) modulation aided multistatic integrated sensing and communication (ISAC) in vehicular networks, whereby its delay-Doppler robustness is exploited for enhanced communication and high-resolution sensing. We present a triangulation-based deployment framework combined with Kalman filtering (KF) that enables accurate target localization and velocity estimation. In addition, we assess the ISAC performance in the multistatic topology to determine its effectiveness in the dynamic environment. Further, a suboptimal placement strategy for the multistatic receivers is devised to reduce the targets' localization error. Numerical results demonstrate significant reductions in the sensing error and bit error rate (BER) performances.