发表机构
Indian Institute of Technology Bombay（印度理工学院孟买分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种动态广义卡尔曼一致性滤波器，仅利用局部信息计算一致性权重，无需全局网络参数，在切换拓扑的传感器网络中保持估计精度并优于现有分布式滤波器。
AI 中文摘要
分布式状态估计对于监视、自主导航和广域监测等应用至关重要，在这些应用中，传感器代理必须仅使用本地测量和邻居间通信来协同跟踪目标。现有分布式滤波器已被证明即使在稀疏的代理间通信和有限的感知范围内也能实现准确的估计。然而，许多这些方法依赖于与通信图全局属性（如图的最大度）相关的一致性参数，因此对网络拓扑的变化敏感。这一局限性在具有移动代理的传感器网络中尤为显著，因为通信链路会随时间变化。本文提出了一种用于具有切换通信拓扑的传感器网络中目标跟踪的动态广义卡尔曼一致性滤波器。所提出的算法仅使用局部可用量计算基于信息的一致性权重，消除了对全局网络参数的需求。数值模拟表明，所提出的算法在切换网络拓扑下保持估计精度，并在给定的跟踪问题中优于现有分布式滤波器。
英文摘要
Distributed state estimation is critical for applications such as surveillance, autonomous navigation, and wide-area monitoring, where sensor agents must cooperatively track targets using only local measurements and neighbor-to-neighbor communication. Existing distributed filters have been shown to achieve accurate estimation even under sparse inter-agent communication and limited sensing ranges. However, many of these methods rely on consensus parameters that depend on global properties of the communication graph, such as the maximum degree of the graph, and are therefore sensitive to changes in network topology. This limitation is particularly significant in sensor networks with mobile agents, where communication links change over time. This paper presents a Dynamic Generalized Kalman Consensus Filter for target tracking in sensor networks with switching communication topologies. The proposed algorithm computes information-based consensus weights using only locally available quantities, eliminating the need for global network parameters. Numerical simulations demonstrate that the proposed algorithm maintains estimation accuracy under switching network topologies and outperforms existing distributed filters in the given tracking problem.
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