Imperial College London
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DrFrancescaBoem

Faculty of Engineering, Department of Electrical and Electronic Engineering

Honorary Lecturer
 
 
 
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Contact

 

f.boem Website

 
 
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Location

 

1108Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Boem:2018:10.1016/j.automatica.2018.03.071,
author = {Boem, F and Zhou, Y and Fischione, C and Parisini, T},
doi = {10.1016/j.automatica.2018.03.071},
journal = {Automatica},
pages = {211--223},
title = {Distributed Pareto-optimal state estimation using sensor networks},
url = {http://dx.doi.org/10.1016/j.automatica.2018.03.071},
volume = {93},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - A  novel  model-based  dynamic  distributed  state  estimator  is  proposed  using  sensor  networks.  The  estimator  consists  of  afiltering  step  –  which  uses  a  weighted  combination  of  sensors  information  –  and  a  model-based  predictor  of  the  system’sstate. The filtering weights and the model-based prediction parameters jointly minimize both the bias and the variance of theprediction error in a Pareto framework at each time-step. The simultaneous distributed design of the filtering weights and ofthe model-based prediction parameters is considered, differently from what is normally done in the literature. It is assumedthat  the  weights  of  the  filtering  step  are  in  general  unequal  for  the  different  state  components,  unlike  existing  consensus-based approaches. The state, the measurements, and the noise components are allowed to be individually correlated, but noprobability  distribution  knowledge  is  assumed  for  the  noise  variables.  Each  sensor  can  measure  only  a  subset  of  the  statevariables. The convergence properties of the mean and of the variance of the prediction error are demonstrated, and they holdboth for the global and the local estimation errors at any network node. Simulation results illustrate the performance of theproposed method, obtaining better results than the state of the art distributed estimation approaches.
AU  - Boem,F
AU  - Zhou,Y
AU  - Fischione,C
AU  - Parisini,T
DO  - 10.1016/j.automatica.2018.03.071
EP  - 223
PY  - 2018///
SN  - 0005-1098
SP  - 211
TI  - Distributed Pareto-optimal state estimation using sensor networks
T2  - Automatica
UR  - http://dx.doi.org/10.1016/j.automatica.2018.03.071
UR  - http://hdl.handle.net/10044/1/57689
VL  - 93
ER  -
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