发表机构
University of Padova; DISI, University of Trento（帕多瓦大学; 特伦托大学 DISI）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种结合IMU与扩展卡尔曼滤波的ISAC框架，在时钟异步且TX移动的双基地场景中联合估计目标多普勒频率及TX运动状态，鲁棒于无静态路径，仿真验证中位多普勒误差1.2%。
AI 中文摘要
我们提出了一种集成感知与通信（ISAC）框架，用于在双基地场景中联合估计被动移动目标的多普勒频率，该场景具有时钟异步节点，且接收器（RX）静止而发射器（TX）移动。在这种设置下，先前的几何解决方案与发射器处的惯性测量单元（IMU）设备集成，形成了一种基于扩展卡尔曼滤波的真正联合数据融合与估计算法。该方法联合估计目标多普勒频率以及发射器的运动速度和方向，并且对发射器与接收器对之间静态路径不可用的情况具有鲁棒性，而这种情况使得先前的解决方案失效。所开发的扩展卡尔曼滤波器在ISAC精度与IMU可靠性之间取得了平衡。所提出的解决方案通过数值模拟进行了验证，在现实工作条件下，使用智能手机级IMU获得了1.2%的中位多普勒误差。
英文摘要
We present an Integrated Sensing And Communication (ISAC) framework for the joint estimation of the Doppler frequency of a passive mobile target in a bistatic scenario with clock-asynchronous nodes and where the Receiver (RX) is static but the Transmitter (TX) is mobile. In such a setup, previous geometric solutions are integrated with an Inertial Measurement Unit (IMU) device at the TX, coming up with a truly joint data fusion and estimation algorithm based on extended Kalman filtering. The approach jointly estimates the target Doppler frequency, along with the speed and direction of motion of the TX and it is robust to the unavailability of static paths between the TX and RX pair, a condition that makes previous solutions ineffective. The developed extended Kalman filter strikes a balance between the accuracy of ISAC and the IMU reliability. The proposed solution is validated via numerical simulations, obtaining a median Doppler error of 1.2% with a smartphone-grade IMU under realistic operating conditions.
Comments6 pages, 5 figures. Submitted to IEEE VTC2027-Spring
