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Please use this identifier to cite or link to this item: http://hdl.handle.net/10481/49086

Title: Optimal Fusion Estimation with Multi-Step Random Delays and Losses in Transmission
Authors: Caballero-Águila, R.
Hermoso-Carazo, Aurora
Linares-Pérez, Josefa
Issue Date: 18-May-2017
Abstract: 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.
Sponsorship: This research is supported by the “Ministerio de Economía y Competitividad” and “Fondo Europeo de Desarrollo Regional” FEDER (Grant No. MTM2014-52291-P).
Publisher: MDPI
Keywords: Recursive fusion estimation
Sensor networks
Random parameter matrices
Random delays
Packet dropouts
URI: http://hdl.handle.net/10481/49086
ISSN: 1424-8220
Citation: 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]
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