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Please use this identifier to cite or link to this item: http://hdl.handle.net/10481/33538

Title: A new estimation algorithm from measurements with multiple-step random delays and packet dropouts
Authors: Caballero-Águila, R.
Hermoso-Carazo, Aurora
Linares-Pérez, Josefa
Issue Date: 2010
Abstract: The least-squares linear estimation problem using covariance information is addressed in discrete-time linear stochastic systems with bounded random observation delays which can lead to bounded packet dropouts. A recursive algorithm, including the computation of predictor, filter, and fixed-point smoother, is obtained by an innovation approach. The random delays are modeled by introducing some Bernoulli random variables with known distributions in the system description. The derivation of the proposed estimation algorithm does not require full knowledge of the state-space model generating the signal to be estimated, but only the delay probabilities and the covariance functions of the processes involved in the observation equation.
Sponsorship: This research is supported by Ministerio de Educación y Ciencia (Grant no. MTM2008-05567) and Junta de Andalucía (Grant no. P07-FQM-02701).
Publisher: Hindawi Publishing Corporation
Keywords: Least-squares linear estimation
Algorithm
URI: http://hdl.handle.net/10481/33538
ISSN: 1024-123X
Citation: Caballero-Águila, R.; Hermoso-Carazo, A.; Linares-Pérez, J. A new estimation algorithm from measurements with multiple-step random delays and packet dropouts. Mathematical Problems in Engineering, 2010: 258065 (2010). [http://hdl.handle.net/10481/33538]
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