AI 中文总结
研究网络ISAC中同步问题，提出SCPD算法分离传感信道多径分量，实现联合同步与多目标参数估计，建立理论条件，还提出多目标跟踪算法和自适应波束形成方案，仿真表明算法在同步和传感上精度高、鲁棒性强，优于传统方法。
AI 中文摘要
网络集成感知与通信（ISAC）为下一代无线系统提供了巨大潜力，通过多基站协作利用空间分集来扩大覆盖范围并增强感知性能。但网络ISAC中的精确感知需要基站间的时频同步，现有同步方法受传感信道压缩引起的多径干扰影响。本文提出结构化典范多向分解（SCPD）算法，有效分离传感信道的多径分量，实现联合网络级同步和多目标参数估计，建立了理论可识别性条件并证明其渐近达到克拉美罗界。还提出多目标跟踪算法及自适应波束形成方案，仿真结果表明所提算法在网络ISAC的同步和传感方面具有卓越的精度和抗异常值鲁棒性，优于传统方法。
英文摘要
Networked integrated sensing and communication (ISAC) offers significant potential for next-generation wireless systems. By exploiting spatial diversity through the cooperation of multiple base stations (BSs), this architecture expands coverage and achieves enhanced sensing performance. However, accurate sensing in networked ISAC requires time-frequency synchronization among BSs. Existing synchronization methods for networked ISAC suffer from inter-path interference caused by sensing channel compression. To address this problem, this paper proposes a structured canonical polyadic decomposition (SCPD) algorithm that effectively separates the multipath components of the sensing channel. Benefiting from this separation, SCPD achieves joint network-level synchronization and multi-target parameter estimation. We establish theoretical identifiability conditions for SCPD and show that it asymptotically achieves the Cramér-Rao bound. Furthermore, by incorporating parameters estimated from different BS pairs, we propose a multi-target tracking algorithm designed for the continuous operation of the system. The proposed algorithm tracks both the trajectories and velocities of moving targets by leveraging geometric diversity. Utilizing tracking results from the previous snapshot, an adaptive beamforming scheme is also developed to improve tracking performance in the next snapshot. Simulation results demonstrate that the proposed algorithms achieve superior accuracy and outlier robustness for both synchronization and sensing in networked ISAC, outperforming traditional approaches.
