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Approximate Distributed Kalman Filtering in Sensor Networks with Quantifiable Performance
Abstract We analyze the performance of an approxima …
We analyze the performance of an approximate distributed Kalman filter proposed in recent work on distributed coordination. This approach to distributed estimation is novel in that it admits a systematic analysis of its performance as various network quantities such as connection density, topology, and bandwidth are varied. Our main contribution is a frequency-domain characterization of the distributed estimator's steady-state performance; this is quantified in terms of a special matrix associated with the connection topology called the graph Laplacian, and also the rate of message exchange between immediate neighbors in the communication network.
te neighbors in the communication network.  +
Authors Demetri P. Spanos, Reza Olfati-Saber, Richard M. Murray  +
ID 2005h  +
Source 2005 International Conference on Information Processing in Sensor Networks (IPSN)  +
Tag som05-ipsn  +
Title Approximate Distributed Kalman Filtering in Sensor Networks with Quantifiable Performance +
Type Conference Paper  +
Categories Papers
Modification date
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15 May 2016 06:17:56  +
URL
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http://www.cds.caltech.edu/~murray/preprints/som05-ipsn.pdf  +
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Approximate Distributed Kalman Filtering in Sensor Networks with Quantifiable Performance + Title
 

 

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