基于雷达网络的分布式目标跟踪
Distributed Target Tracking using Radar Networks
AI总结：
本文提出一种用于FMCW雷达网络的完全分布式目标跟踪框架，开发了D-MAP与D-EKF两种基于共识优化的估计器，仿真显示D-MAP提升了估计精度，可替代集中式雷达跟踪。
AI中文摘要：
分布式目标跟踪对于可扩展且鲁棒的感知系统至关重要，它使多个雷达节点能够协同估计目标状态，无需依赖集中式融合中心。本文提出了一种用于调频连续波（FMCW）雷达网络中单目标跟踪的完全分布式框架，每个单基地雷达节点观测本地距离和多普勒测量值，仅与相邻节点交换信息。本文开发了两种基于共识优化的估计器：第一种是分布式最大后验（D-MAP）估计器，用于基于批量的跟踪，其中融入了先验状态信息，所得到的优化问题采用基于共识的交替方向乘子法（ADMM）求解；第二种是分布式扩展卡尔曼滤波（D-EKF），用于递归跟踪，每个节点执行本地预测与校正，随后进行基于共识ADMM的信息交换。本文推导了后验克拉美罗下界（PCRLB）作为理论性能基准。仿真结果表明，D-MAP相较于分布式最大似然基线提升了估计精度，这些结果证明，在仅存在节点间本地通信的条件下，所提框架可作为集中式雷达跟踪的可扩展且鲁棒的替代方案。
英文摘要：
Distributed target tracking is essential for scalable and robust sensing systems, as it enables multiple radar nodes to cooperatively estimate a target state without relying on a centralized fusion center. In this paper, we present a fully distributed framework for single-target tracking in frequency-modulated continuous-wave (FMCW) radar networks. Each monostatic radar node observes local range and Doppler measurements and exchanges information only with neighboring nodes. Two consensus optimization-based estimators are developed. First, a Distributed Maximum A Posteriori (D-MAP) estimator is formulated for batch-based tracking, where prior state information is incorporated and the resulting optimization problem is solved using consensus-based alternating direction method of multipliers (ADMM). Second, a Distributed Extended Kalman Filter (D-EKF) is proposed for recursive tracking, where each node performs local prediction and correction followed by consensus ADMM-based information exchange. We derive the posterior Cramér-Rao lower bound (PCRLB) as a theoretical performance benchmark. Our simulation results show that D-MAP improves the accuracy of the estimation over the distributed maximum-likelihood baseline. These results demonstrate that the proposed framework provides a scalable and robust alternative to centralized radar tracking, given only local inter-node communication.
