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A Distributed Diffusion Kalman Filter in Multi-TaskNetworks.


Affiliations
1 School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2 School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731., China
3 School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731., China
 

The Distributed Diffusion Kalman Filter (DDKF) algorithm has earned great attention lately and shows an elaborate way to address the issue of distributed optimization over networks. Estimation and tracking of a single state vector collectively by nodes have been the point of focus. However, there are several multi-task-oriented issues where the optimal state vector for each node may not be the same. Thiswork considers sensor networks for distributed multi-task tracking in which individual nodes communicate with their immediate nodes. A diffusion-based distributed multi-task tracking algorithm is developed. This is done by implementing an unsupervised adaptive clustering process, which aids nodes in forming clusters and collaborating on tasks. This gave rise to an effective level of cooperation for improving state vector estimation accuracy, especially in cases where a cluster's background experience is unknown. To demonstrate the efficiency of our algorithm, computer simulations were conducted. Comparison has been carried out for the Diffusion Kalman Filtermulti-task with respect to the Adapt-Then-Combine (ATC) diffusion schemes utilizing both static and adaptive combination weights. Results showed that the ATC diffusion schemes algorithm has great performance with the adaptive combiners as compared to static combiners.

Keywords

Adaptive clustering, Diffusion scheme, Distributed Kalman filtering, Multi-task sensornetworks.
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  • A Distributed Diffusion Kalman Filter in Multi-TaskNetworks.

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Authors

Ijeoma Amuche Chikwendu
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
KulevomeDelanyoKwameBensah
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731., China
ChiagoziemChimaUkwuoma
School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731., China
Chukwuebuka Joseph Ejiyi
School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731., China

Abstract


The Distributed Diffusion Kalman Filter (DDKF) algorithm has earned great attention lately and shows an elaborate way to address the issue of distributed optimization over networks. Estimation and tracking of a single state vector collectively by nodes have been the point of focus. However, there are several multi-task-oriented issues where the optimal state vector for each node may not be the same. Thiswork considers sensor networks for distributed multi-task tracking in which individual nodes communicate with their immediate nodes. A diffusion-based distributed multi-task tracking algorithm is developed. This is done by implementing an unsupervised adaptive clustering process, which aids nodes in forming clusters and collaborating on tasks. This gave rise to an effective level of cooperation for improving state vector estimation accuracy, especially in cases where a cluster's background experience is unknown. To demonstrate the efficiency of our algorithm, computer simulations were conducted. Comparison has been carried out for the Diffusion Kalman Filtermulti-task with respect to the Adapt-Then-Combine (ATC) diffusion schemes utilizing both static and adaptive combination weights. Results showed that the ATC diffusion schemes algorithm has great performance with the adaptive combiners as compared to static combiners.

Keywords


Adaptive clustering, Diffusion scheme, Distributed Kalman filtering, Multi-task sensornetworks.

References