Design of Adaptive Kalman Consensus Filters (a-KCF)

Signals Pub Date : 2023-08-31 DOI:10.3390/signals4030033
Shalin Ye, Shufan Wu
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Abstract

This paper addresses the problem of designing an adaptive Kalman consensus filter (a-KCF) which embedded in multiple mobile agents that are distributed in a 2D domain. The role of such filters is to provide adaptive estimation of the states of a dynamic linear system through communication over a wireless sensor network. It is assumed that each sensing device (embedded in each agent) provides partial state measurements and transmits the information to its instant neighbors in the communication topology. An adaptive consensus algorithm is then adopted to enforce the agreement on the state estimates among all connected agents. The basis of a-KCF design is derived from the classic Kalman filtering theorem; the adaptation of the consensus gain for each local filter in the disagreement terms improves the convergence of the associated difference between the estimation and the actual states of the dynamic linear system, reducing it to zero with appropriate norms. Simulation results testing the performance of a-KCF confirm the validation of our design.
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自适应卡尔曼一致滤波器(a-KCF)设计
本文研究了一种自适应卡尔曼共识滤波器(a- kcf)的设计问题,该滤波器嵌入到分布在二维域中的多个移动代理中。这种滤波器的作用是通过无线传感器网络上的通信提供动态线性系统状态的自适应估计。假设每个感知设备(嵌入在每个代理中)提供部分状态测量,并将信息传输到通信拓扑中的即时邻居。然后,采用自适应共识算法在所有连接的代理之间强制执行状态估计的一致性。a-KCF设计的基础来源于经典的卡尔曼滤波定理;在不一致项中对每个局部滤波器的一致性增益进行自适应,提高了动态线性系统的估计与实际状态之间的相关差的收敛性,并在适当的范数下将其降至零。仿真结果验证了a-KCF性能的有效性。
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CiteScore
3.20
自引率
0.00%
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0
审稿时长
11 weeks
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