最优部署的多静态OTFS-ISAC设计与基于卡尔曼的目标跟踪
Optimally Deployed Multistatic OTFS-ISAC Design With Kalman-Based Tracking of Targets
AI总结：
本文提出基于卡尔曼滤波的多静态OTFS-ISAC部署方法，通过优化接收器布局提升目标定位和通信性能。
摘要：
本文研究了正交时频空间（OTFS）调制辅助的多静态集成感知与通信（ISAC）在车辆网络中的应用，其中其延迟-多普勒鲁棒性被用于增强通信和高分辨率感知。我们提出了一种基于三角定位的部署框架结合卡尔曼滤波（KF），以实现准确的目标定位和速度估计。此外，我们评估了多静态拓扑结构下的ISAC性能，以确定其在动态环境中的有效性。进一步，我们设计了一种子最优的多静态接收器布置策略，以减少目标的定位误差。数值结果表明，传感误差和比特误差率（BER）性能有显著降低。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
