Kalman Filtering based Target Tracking for Multistatic Sensing in ISAC Systems
Quan Yuan, Shun Zhuge, Zhiping Lin, Yugang Ma, Yonghong Zeng · 2025
The advancement of Integrated Sensing and Communication (ISAC) has facilitated the adoption of target tracking technologies, yet most existing research focuses on waveform design rather than specific tracking algorithms within ISAC systems. To address this gap, this paper proposes an effective target tracking method based on Kalman filter (KF) algorithms within the ISAC framework. The method filters noisy radar measurements, such as bistatic range (BR), bistatic range rate (BRR), and direction of arrival (DOA), to enhance tracking accuracy. Performance is assessed by using trajectory maps and root-meansquare error (RMSE) curves, demonstrating that the proposed method significantly improves estimation accuracy while increasing robustness to measurement noise, especially in DOA. Compared to our previously proposed geometric target localization method, this approach delivers superior tracking performance, making it highly suitable for real-time applications in ISAC systems.