In this section
@inproceedings{Boem:2015:10.1109/ECC.2015.7331101,
author = {Boem, F and Xu, Y and Fischione, C and Parisini, T},
doi = {10.1109/ECC.2015.7331101},
pages = {3673--3680},
publisher = {IEEE},
title = {A distributed pareto-optimal dynamic estimation method},
url = {http://dx.doi.org/10.1109/ECC.2015.7331101},
year = {2015}
}
TY - CPAPER
AB - In this paper, a novel distributed model-based prediction method is proposed using sensor networks. Each sensor communicates with the neighboring nodes for state estimation based on a consensus protocol without centralized coordination. The proposed distributed estimator consists of a consensus-filtering scheme, which uses a weighted combination of sensors information, and a model-based predictor. Both the consensus-filtering weights and the model-based prediction parameter for all the state components are jointly optimized to minimize the variance and bias of the prediction error in a Pareto framework. It is assumed that the weights of the consensus-filtering phase are unequal for the different state components, unlike consensus-based approaches from literature. The state, the measurements, and the noise components are assumed to be individually correlated, but no probability distribution knowledge is assumed for the noise variables. The optimal weights are derived and it is established that the consensus-filtering weights and the model-based prediction parameters cannot be designed separately in an optimal way. The asymptotic convergence of the mean of the prediction error is demonstrated. Simulation results show the performance of the proposed method, obtaining better results than distributed Kalman filtering.
AU - Boem,F
AU - Xu,Y
AU - Fischione,C
AU - Parisini,T
DO - 10.1109/ECC.2015.7331101
EP - 3680
PB - IEEE
PY - 2015///
SP - 3673
TI - A distributed pareto-optimal dynamic estimation method
UR - http://dx.doi.org/10.1109/ECC.2015.7331101
UR - http://hdl.handle.net/10044/1/39116
ER -