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Diffusion Kalman Filtering Based on Covariance Intersection
Hu, Jinwen ; Xie, Lihua ; Zhang, Cishen
IEEE transactions on signal processing, 2012-02, Vol.60 (2), p.891-902
[Periódico revisado por pares]
New York, NY: IEEE
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Título:
Diffusion Kalman Filtering Based on Covariance Intersection
Autor:
Hu, Jinwen
;
Xie, Lihua
;
Zhang, Cishen
Assuntos:
Algorithm design and analysis
;
Algorithms
;
Applied sciences
;
Communication systems
;
Covariance
;
Detection, estimation, filtering, equalization, prediction
;
Diffusion
;
Estimates
;
Estimation error
;
Exact sciences and
technology
;
Information, signal and communications theory
;
Intersections
;
Kalman filtering
;
Kalman filters
;
Network topology
;
Sampling
;
Sampling, quantization
;
Signal and communications theory
;
Signal, noise
;
Telecommunications and information theory
;
Time measurement
;
Topology
É parte de:
IEEE transactions on signal processing, 2012-02, Vol.60 (2), p.891-902
Notas:
ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
Descrição:
This paper is concerned with distributed Kalman filtering for linear time-varying systems over multiagent sensor networks. We propose a diffusion Kalman filtering algorithm based on the covariance intersection method, where local estimates are fused by incorporating the covariance information of local Kalman filters. Our algorithm leads to a stable estimate for each agent regardless of whether the system is uniformly observable locally by the measurements of its neighbors which include the measurements of itself as long as the system is uniformly observable by the measurements of all the agents and the communication is sufficiently fast compared to the sampling. Simulation results validate the effectiveness of the proposed distributed Kalman filtering algorithm.
Editor:
New York, NY: IEEE
Idioma:
Inglês
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