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Optimal Fusion Estimation with Multi-Step Random Delays and Losses in Transmission

[PDF] CaballeroAguila_OptimalFusion.pdf (493.5Kb)
Identificadores
URI: http://hdl.handle.net/10481/49086
DOI: 10.3390/s17051151
ISSN: 1424-8220
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Autor
Caballero-Águila, R.; Hermoso-Carazo, Aurora; Linares-Pérez, Josefa
Editorial
MDPI
Materia
Recursive fusion estimation
 
Sensor networks
 
Random parameter matrices
 
Random delays
 
Packet dropouts
 
Fecha
2017-05-18
Referencia bibliográfica
Caballero-Águila, R.; Hermoso-Carazo, A.; Linares-Pérez, J. Optimal Fusion Estimation with Multi-Step Random Delays and Losses in Transmission. Sensors, 17(5): 1151 (2017). [http://hdl.handle.net/10481/49086]
Patrocinador
This research is supported by the “Ministerio de Economía y Competitividad” and “Fondo Europeo de Desarrollo Regional” FEDER (Grant No. MTM2014-52291-P).
Resumen
This paper is concerned with the optimal fusion estimation problem in networked stochastic systems with bounded random delays and packet dropouts, which unavoidably occur during the data transmission in the network. The measured outputs from each sensor are perturbed by random parameter matrices and white additive noises, which are cross-correlated between the different sensors. Least-squares fusion linear estimators including filter, predictor and fixed-point smoother, as well as the corresponding estimation error covariance matrices are designed via the innovation analysis approach. The proposed recursive algorithms depend on the delay probabilities at each sampling time, but do not to need to know if a particular measurement is delayed or not. Moreover, the knowledge of the signal evolution model is not required, as the algorithms need only the first and second order moments of the processes involved. Some of the practical situations covered by the proposed system model with random parameter matrices are analyzed and the influence of the delays in the estimation accuracy are examined in a numerical example.
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