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Asynchronous distributed state estimation based on a continuous-time stochastic model
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DOI：10.1002/acs.1290.png)
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Abstract
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In this paper, the problem of state estimation in an asynchronous distributed multi-sensor estimation (ADE) system is considered. In such an ADE system, the state of a plant of interest is estimated by a group of local estimators. Each local estimator based, for example, on a Kalman filter, performs fusion of data from its local sensor and other (remote) processors to compute possibly best state estimates. In performing data fusion, however, two important issues need to be addressed, namely, the problem of asynchronism of local processors and the one of unknown correlation between asynchronous data in local processors. Consequently, there are two main contributions proposed in this paper. The first is a method to deal with asynchronous discrete-time data based on a continuous-time stochastic plant model. The second contribution is an asynchronous distributed data-fusion algorithm. Simulated experiments illustrate the effectiveness of the proposed ADE approach. Copyright (c) 2011 John Wiley & Sons, Ltd.
Keywords:
Gaussian processes
distributed systems
state estimation
Kalman filtering
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