Name
Title | ||
|---|---|---|
Distributed joint estimation and identification for sensor networks with unknown inputs |
Abstract | ||
|---|---|---|
In this paper we consider the problem of distributed, joint, state estimation and identification for a class of stochastic systems with unknown inputs (UI). A distributed Expectation-Maximization (EM) algorithm is presented to estimate the local state at each sensor node by using the local observations in the E-step, and three different consensus schemes are proposed to diffuse the local state estimate to each sensor's neighbours and to derive the global state estimate at each node. In the M-step, each sensor identifies the local unknown inputs by using the global state estimate. A numerical example of target tracking in distributed sensor network is given to verify the three different distributed EM algorithms compared with the centralized EM based measurement-level and track-level fusion. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ | ISSNIP |
Keywords | Field | DocType |
distributed expectation-maximization algorithm,distributed sensor network,expectation-maximisation algorithm,m-step,distributed joint estimation-identification,stochastic systems,target tracking,state estimation,track-level fusion,centralized em based measurement-level,global state estimate,e-step,wireless sensor networks,em algorithm,sensor fusion,kalman filters,computer architecture | Sensor node,Computer science,Expectation–maximization algorithm,Control theory,Brooks–Iyengar algorithm,Kalman filter,Real-time computing,Sensor fusion,Wireless sensor network,Distributed computing | Conference |
Citations | PageRank | References |
1 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hua Lan | 1 | 5 | 2.40 |
| Adrian N. Bishop | 2 | 1 | 0.34 |
| Quan Pan | 3 | 568 | 47.06 |