Abstract
This paper presents the decentralized state estimation problem of discrete-time nonlinear systems with randomly delayed measurements in sensor networks. In this problem, measurement data from the sensor network is sent to a remote processing network via data transmission network, with random measurement delays obeying a Markov chain. Here, we present the Gaussian-consensus filter (GCF) to pursue a tradeoff between estimate accuracy and computing time. It includes a novel Gaussian approximated filter with estimated delay probability (GEDPF) online in the sense of minimizing the estimate error covariance in each local processing unit (PU), and a consensus strategy among PUs in processing network to give a fast decentralized fusion. A numerical example with different measurement delays is simulated to validate the proposed method.
Original language | English |
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Pages (from-to) | 91-102 |
Number of pages | 12 |
Journal | Information Fusion |
Volume | 30 |
DOIs | |
State | Published - 1 Jul 2016 |
Keywords
- Consensus filter
- Gaussian approximated filter
- Markov chain
- Randomly delayed measurements
- Sensor networks