Name
Abstract | ||
|---|---|---|
In this paper, we introduce three novel distributed Kalman filtering (DKF) algorithms for sensor networks. The first algorithm is a modification of a previous DKF algorithm presented by the author in CDC-ECC '05. The previous algo- rithm was only applicable to sensors with identical observation matrices which meant the process had to be observable by every sensor. The modified DKF algorithm uses two identical consensus filters for fusion of the sensor data and covariance information and is applicable to sensor networks with different observation matrices. This enables the sensor network to act as a collective observer for the processes occurring in an environment. Then, we introduce a continuous-time distributed Kalman filter that uses local aggregation of the sensor data but attempts to reach a consensus on estimates with other nodes in the network. This peer-to-peer distributed estimation method gives rise to two iterative distributed Kalman filtering algorithms with different consensus strategies on estimates. Communication complexity and packet-loss issues are dis- cussed. The performance and effectiveness of these distributed Kalman filtering algorithms are compared and demonstrated on a target tracking task. Index Terms—sensor networks, distributed Kalman filtering, consensus filtering, sensor fusion |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/CDC.2007.4434303 | New Orleans, LA |
Keywords | Field | DocType |
Kalman filters,communication complexity,continuous time filters,covariance matrices,distributed algorithms,distributed tracking,iterative methods,sensor fusion,wireless sensor networks,collective observer,communication complexity,consensus filter strategy,continuous-time distributed Kalman filter,covariance matrix,iterative distributed Kalman filtering algorithm,packet-loss issue,peer-to-peer distributed estimation method,sensor data fusion,target tracking task,wireless sensor network,consensus filtering,distributed Kalman filtering,sensor fusion,sensor networks | Fast Kalman filter,Computer science,Control theory,Iterative method,Brooks–Iyengar algorithm,Kalman filter,Communication complexity,Sensor fusion,Distributed algorithm,Wireless sensor network | Conference |
ISSN | ISBN | Citations |
0191-2216 E-ISBN : 978-1-4244-1498-7 | 978-1-4244-1498-7 | 417 |
PageRank | References | Authors |
19.85 | 14 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Reza Olfati-Saber | 1 | 8066 | 549.43 |