Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15814
Title: Multiresolution modeling and estimation of multisensor data
Authors: Zhang, L 
Wu, X
Pan, Q
Zhang, H
Issue Date: 2004
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on signal processing, 2004, v. 52, no. 11, p. 3170-3182 How to cite?
Journal: IEEE transactions on signal processing 
Abstract: This paper presents a multiresolution multisensor data fusion scheme for dynamic systems to be observed by several sensors of different resolutions. A state projection equation is introduced to associate the states of a system at each resolution with others. This projection equation together with the state transition equation and the measurement equations at each of the resolutions construct a continuous-time model of the system. The model meets the requirements of Kalman filtering. In discrete time, the state transition is described at the finest resolution and the sampling frequencies of sensors decrease successively by a factor of two in resolution. We can build a discrete model of the system by using a linear projection operator to approximate the state space projection. This discrete model satisfies the requirements of discrete Kalman filtering, which actually offers an optimal estimation algorithm of the system. In time-invariant case, the stability of the Kalman filter is analyzed and a sufficient condition for the filtering stability is given. A Markov-process-based example is given to illustrate and evaluate the proposed scheme of multiresolution modeling and estimation with multiple sensors.
URI: http://hdl.handle.net/10397/15814
ISSN: 1053-587X
EISSN: 1941-0476
DOI: 10.1109/TSP.2004.836538
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