Paper Info
Title
Unscented-Transformation-Based Distributed Nonlinear State Estimation: Algorithm, Analysis, and Experiments
Abstract
The problem of fully distributed state estimation using networked local sensors is studied in this paper. Specifically, the scenario with general nonlinear process model and local sensing models is considered by extending the distributed hybrid information fusion (DHIF) algorithm proposed by Wang and Ren. Different from the extended Kalman filter-based approaches which require the computation of the Jacobian matrix at every time instant, the unscented transformation (UT) approach is adopted for such an extension to better characterize the statistics after nonlinear transformations. The extended algorithm inherits the advantages of the original DHIF algorithm for requiring only one communication iteration between every two consecutive time instants and for requiring no global information. As well recognized that the stability analysis in the distributed UT-based framework is challenging, in the special case where the sensing models are linear, it is also analytically shown that the local estimate errors are bounded in the mean square sense. Simulations are extensively studied to show the performance of the extended algorithm. More importantly, the effectiveness of the algorithm is also verified using real data collected in a robot tracking task with networked Vicon cameras.
Year
DOI
Venue
2019
10.1109/tcst.2018.2847290
IEEE Transactions on Control Systems and Technology
Keywords
Field
DocType
Sensors,Kalman filters,Estimation,Approximation algorithms,Random variables,Computational modeling,Stability analysis
Approximation algorithm,Extended Kalman filter,Random variable,Nonlinear system,Jacobian matrix and determinant,Control theory,Algorithm,Kalman filter,Mathematics,Bounded function,Computation
Journal
Volume
Issue
ISSN
27
5
1063-6536
Citations 
PageRank 
References 
1
0.35
0
Authors
3
Name
Order
Citations
PageRank
Shao-Cheng Wang1435.63
Yang Lyu231.06
Wei Ren35238250.63