Centralized Fusion Approach to the Estimation Problem with Multi-Packet Processing under Uncertainty in Outputs and Transmissions
Metadata
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MDPI
Materia
Least-squares filtering Least-squares smoothing Networked systems Random parameter matrices Random delays Packet dropouts
Date
2018Referencia bibliográfica
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.
Sponsorship
This research is supported by Ministerio de Economía, Industria y Competitividad, Agencia Estatal de Investigación and Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2017-84199-P).Abstract
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.