A Cooperative ISAC Tracking System for Complex Trajectories

Due to the ability to provide exceptional dual-functional gains, integrated sensing and communication (ISAC) is currently recognized as a critical component of 6G vehicle-to-everything (V2X) networks. Additionally, cloud radio access networks (C-RANs) enable multi-base station (BS) cooperation, which also contributes to enhancing dual performance. Inspired by these technologies, this paper investigates a multi-BS cooperative ISAC (Co-ISAC) tracking system based on the C-RAN architecture. This system is capable of accurately tracking the vehicle user (VU) while simultaneously providing it with high-rate services. Considering two types of VU state evolution models, i.e., linear motion (LM) and turning motion (TM), we develop the tracking system using the interactive multiple model unscented Kalman filter (IMM-UKF) framework. Furthermore, we design the overall workflow of the system based on weighted least squares (WLS). Finally, numerical results demonstrate that our tracking system exhibits superior tracking performance compared to other baseline schemes.

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Publication Details

Published
2025-06-08
DOI
https://doi.org/10.1109/iccworkshops67674.2025.11162225
Primary Topic
Fault Detection and Control Systems
Type
article
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article

A Cooperative ISAC Tracking System for Complex Trajectories

Shaoyi Xu, Hanxue Ding, Ziheng Xu
Fault Detection and Control Systems
article

A Cooperative ISAC Tracking System for Complex Trajectories

Shaoyi Xu, Hanxue Ding, Ziheng Xu
article en

Abstract

Due to the ability to provide exceptional dual-functional gains, integrated sensing and communication (ISAC) is currently recognized as a critical component of 6G vehicle-to-everything (V2X) networks. Additionally, cloud radio access networks (C-RANs) enable multi-base station (BS) cooperation, which also contributes to enhancing dual performance. Inspired by these technologies, this paper investigates a multi-BS cooperative ISAC (Co-ISAC) tracking system based on the C-RAN architecture. This system is capable of accurately tracking the vehicle user (VU) while simultaneously providing it with high-rate services. Considering two types of VU state evolution models, i.e., linear motion (LM) and turning motion (TM), we develop the tracking system using the interactive multiple model unscented Kalman filter (IMM-UKF) framework. Furthermore, we design the overall workflow of the system based on weighted least squares (WLS). Finally, numerical results demonstrate that our tracking system exhibits superior tracking performance compared to other baseline schemes.

Beijing Jiaotong University (CN)
Openalex Percentile: Top 68%
Fault Detection and Control Systems
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A Cooperative ISAC Tracking System for Complex Trajectories — Shaoyi Xu, Hanxue Ding, et al. · (2025) | TGRS Research Map | TGRS