Design of Adaptive Kalman Consensus Filters (a-KCF)

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 wir...

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Main Authors: Shalin Ye, Shufan Wu
Format: Article
Language:English
Published: MDPI AG 2023-08-01
Series:Signals
Subjects:
Online Access:https://www.mdpi.com/2624-6120/4/3/33
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author Shalin Ye
Shufan Wu
author_facet Shalin Ye
Shufan Wu
author_sort Shalin Ye
collection DOAJ
description 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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spelling doaj.art-7ecd21a657494c29bde4cb52ab2bdb932023-11-19T12:58:28ZengMDPI AGSignals2624-61202023-08-014361762910.3390/signals4030033Design of Adaptive Kalman Consensus Filters (a-KCF)Shalin Ye0Shufan Wu1Department of Aerospace Information and Control, School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, ChinaDepartment of Aerospace Information and Control, School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, ChinaThis 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.https://www.mdpi.com/2624-6120/4/3/33adaptive consensus filtersKalman filtersdistributed systemcommunication topology
spellingShingle Shalin Ye
Shufan Wu
Design of Adaptive Kalman Consensus Filters (a-KCF)
Signals
adaptive consensus filters
Kalman filters
distributed system
communication topology
title Design of Adaptive Kalman Consensus Filters (a-KCF)
title_full Design of Adaptive Kalman Consensus Filters (a-KCF)
title_fullStr Design of Adaptive Kalman Consensus Filters (a-KCF)
title_full_unstemmed Design of Adaptive Kalman Consensus Filters (a-KCF)
title_short Design of Adaptive Kalman Consensus Filters (a-KCF)
title_sort design of adaptive kalman consensus filters a kcf
topic adaptive consensus filters
Kalman filters
distributed system
communication topology
url https://www.mdpi.com/2624-6120/4/3/33
work_keys_str_mv AT shalinye designofadaptivekalmanconsensusfiltersakcf
AT shufanwu designofadaptivekalmanconsensusfiltersakcf