View source for Approximate Distributed Kalman Filtering in Sensor Networks with Quantifiable Performance

{{HTDB paper
| authors = Demetri P. Spanos, Reza Olfati-Saber, Richard M. Murray
| title = Approximate Distributed Kalman Filtering in Sensor Networks with Quantifiable Performance
| source = 2005 International Conference on Information Processing in Sensor Networks (IPSN)
| year = 2005
| type = Conference Paper
| funding = AFOSR/info2, MURI/coop
| url = http://www.cds.caltech.edu/~murray/preprints/som05-ipsn.pdf
| abstract = 
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.

| flags = 
| tag = som05-ipsn
| id = 2005h
}}