Centralized filtering and smoothing algorithms from outputs with random parameter matrices transmitted through uncertain communication channels

The least-squares linear centralized estimation problem is addressed for discrete-time signals from measured outputs whose disturbances are modeled by random parameter matrices and correlated noises. These measurements, coming from different sensors, are sent to a processing center to obtain the est...

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Vydané v:Digital signal processing Ročník 85; s. 77 - 85
Hlavní autori: Caballero-Águila, R., Hermoso-Carazo, A., Linares-Pérez, J.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Inc 01.02.2019
Predmet:
Centralized fusion estimation
Packet dropouts
Random delays
Random parameter matrices
Uncertain observations
Random parameter matrices
Uncertain observations
Random delays
Centralized fusion estimation
Packet dropouts
ISSN:1051-2004, 1095-4333
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Popis
Shrnutí:The least-squares linear centralized estimation problem is addressed for discrete-time signals from measured outputs whose disturbances are modeled by random parameter matrices and correlated noises. These measurements, coming from different sensors, are sent to a processing center to obtain the estimators and, due to random transmission failures, some of the data packet processed for the estimation may either contain only noise (uncertain observations), be delayed (sensor delays) or even be definitely lost (packet dropouts). Different sequences of Bernoulli random variables with known probabilities are employed to describe the multiple random transmission uncertainties of the different sensors. Using the last observation that successfully arrived when a packet is lost, the optimal linear centralized fusion estimators, including filter, multi-step predictors and fixed-point smoothers, are obtained via an innovation approach; this approach is a general and useful tool to find easily implementable recursive algorithms for the optimal linear estimators under the least-squares optimality criterion. The proposed algorithms are obtained without requiring the evolution model of the signal process, but using only the first and second-order moments of the processes involved in the measurement model.
ISSN:1051-2004
1095-4333
DOI:10.1016/j.dsp.2018.11.010