Lian Bosen, Wan Yan, Zhang Ya, Liu Mushuang, Lewis Frank L, Chai Tianyou
IEEE Trans Cybern. 2022 Jun;52(6):5242-5254. doi: 10.1109/TCYB.2020.3029007. Epub 2022 Jun 16.
Consensus-based distributed Kalman filters for estimation with targets have attracted considerable attention. Most of the existing Kalman filters use the average consensus approach, which tends to have a low convergence speed. They also rarely consider the impacts of limited sensing range and target mobility on the information flow topology. In this article, we address these issues by designing a novel distributed Kalman consensus filter (DKCF) with an information-weighted consensus structure for random mobile target estimation in continuous time. A new moving target information-flow topology for the measurement of targets is developed based on the sensors' sensing ranges, targets' random mobility, and local information-weighted neighbors. Novel necessary and sufficient conditions about the convergence of the proposed DKCF are developed. Under these conditions, the estimates of all sensors converge to the consensus values. Simulation and comparative studies show the effectiveness and the superiority of this new DKCF.
基于共识的带目标估计分布式卡尔曼滤波器已引起广泛关注。现有的大多数卡尔曼滤波器采用平均共识方法，其收敛速度往往较低。它们也很少考虑有限传感范围和目标移动性对信息流拓扑结构的影响。在本文中，我们通过设计一种新颖的分布式卡尔曼共识滤波器（DKCF）来解决这些问题，该滤波器具有信息加权共识结构，用于连续时间随机移动目标估计。基于传感器的传感范围、目标的随机移动性和局部信息加权邻居，开发了一种用于目标测量的新移动目标信息流拓扑结构。推导了所提出的DKCF收敛的新颖充要条件。在这些条件下，所有传感器的估计值收敛到共识值。仿真和对比研究表明了这种新型DKCF的有效性和优越性。
