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Open AccessArticle

Centralized Fusion Approach to the Estimation Problem with Multi-Packet Processing under Uncertainty in Outputs and Transmissions

1
Departamento de Estadística, Universidad de Jaén, Paraje Las Lagunillas, 23071 Jaén, Spain
2
Departamento de Estadística, Universidad de Granada, Avda. Fuentenueva, 18071 Granada, Spain
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2018, 18(8), 2697; https://doi.org/10.3390/s18082697
Received: 8 July 2018 / Revised: 12 August 2018 / Accepted: 13 August 2018 / Published: 16 August 2018
(This article belongs to the Collection Multi-Sensor Information Fusion)
This paper is concerned with the least-squares linear centralized estimation problem in multi-sensor network systems from measured outputs with uncertainties modeled by random parameter matrices. These measurements are transmitted to a central processor over different communication channels, and owing to the unreliability of the network, random one-step delays and packet dropouts are assumed to occur during the transmissions. In order to avoid network congestion, at each sampling time, each sensor’s data packet is transmitted just once, but due to the uncertainty of the transmissions, the processing center may receive either one packet, two packets, or nothing. Different white sequences of Bernoulli random variables are introduced to describe the observations used to update the estimators at each sampling time. To address the centralized estimation problem, augmented observation vectors are defined by accumulating the raw measurements from the different sensors, and when the current measurement of a sensor does not arrive on time, the corresponding component of the augmented measured output predictor is used as compensation in the estimator design. Through an innovation approach, centralized fusion estimators, including predictors, filters, and smoothers are obtained by recursive algorithms without requiring the signal evolution model. A numerical example is presented to show how uncertain systems with state-dependent multiplicative noise can be covered by the proposed model and how the estimation accuracy is influenced by both sensor uncertainties and transmission failures. View Full-Text
Keywords: least-squares filtering; least-squares smoothing; networked systems; random parameter matrices; random delays; packet dropouts least-squares filtering; least-squares smoothing; networked systems; random parameter matrices; random delays; packet dropouts
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Caballero-Águila, R.; Hermoso-Carazo, A.; Linares-Pérez, J. Centralized Fusion Approach to the Estimation Problem with Multi-Packet Processing under Uncertainty in Outputs and Transmissions. Sensors 2018, 18, 2697.

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