Distributed Kalman Filter with Embedded Consensus Filters Export

@inproceedings{OlfatiSaber_CDC05, abstract = {The problem of distributed Kalman filtering (DKF) for sensor networks is one of the most fundamental distributed estimation problems for scalable sensor fusion. This paper addresses the DKF problem by reducing it to two separate dynamic consensus problems in terms of weighted measurements and inverse-covariance matrices. These to data fusion problems are solved is a distributed way using low-pass and band-pass consensus filters. Consensus filters are distributed algorithms that allow calculation of average-consensus of time-varying signals. The stability properties of consensus filters is discussed in a companion CDC \&\#8217;05 paper [24]. We show that a central Kalman filter for sensor networks can be decomposed into n micro-Kalman filters with inputs that are provided by two types of consensus filters. This network of micro-Kalman filters collectively are capable to provide an estimate of the state of the process (under observation) that is identical to the estimate obtained by a central Kalman filter given that all nodes agree on two central sums. Later, we demonstrate that our consensus filters can approximate these sums and that gives an approximate distributed Kalman filtering algorithm. A detailed account of the computational and communication architecture of the algorithm is provided. Simulation results are presented for a sensor network with 200 nodes and more than 1000 links.}, author = {Olfati-Saber, R.}, booktitle = {Decision and Control, 2005 and 2005 European Control Conference. CDC-ECC '05. 44th IEEE Conference on}, citeulike-article-id = {3431124}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1583486}, journal = {Decision and Control, 2005 and 2005 European Control Conference. CDC-ECC '05. 44th IEEE Conference on}, keywords = {consensus, distributed\_filtering, distributed\_kalman\_filtering}, pages = {8179--8184}, posted-at = {2008-10-20 16:10:57}, priority = {2}, title = {Distributed Kalman Filter with Embedded Consensus Filters}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1583486}, year = {2005} }

TY - CONF ID - OlfatiSaber_CDC05 L3 - citeulike-article-id:3431124 N2 - The problem of distributed Kalman filtering (DKF) for sensor networks is one of the most fundamental distributed estimation problems for scalable sensor fusion. This paper addresses the DKF problem by reducing it to two separate dynamic consensus problems in terms of weighted measurements and inverse-covariance matrices. These to data fusion problems are solved is a distributed way using low-pass and band-pass consensus filters. Consensus filters are distributed algorithms that allow calculation of average-consensus of time-varying signals. The stability properties of consensus filters is discussed in a companion CDC ’05 paper [24]. We show that a central Kalman filter for sensor networks can be decomposed into n micro-Kalman filters with inputs that are provided by two types of consensus filters. This network of micro-Kalman filters collectively are capable to provide an estimate of the state of the process (under observation) that is identical to the estimate obtained by a central Kalman filter given that all nodes agree on two central sums. Later, we demonstrate that our consensus filters can approximate these sums and that gives an approximate distributed Kalman filtering algorithm. A detailed account of the computational and communication architecture of the algorithm is provided. Simulation results are presented for a sensor network with 200 nodes and more than 1000 links. JF - Decision and Control, 2005 and 2005 European Control Conference. CDC-ECC '05. 44th IEEE Conference on EP - 8184 TI - Distributed Kalman Filter with Embedded Consensus Filters T2 - Decision and Control, 2005 and 2005 European Control Conference. CDC-ECC '05. 44th IEEE Conference on SP - 8179 KW - consensus KW - distributed_filtering KW - distributed_kalman_filtering AU - Olfati-Saber, R PY - 2005/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1583486 ER -