A new estimation algorithm from measurements with multiple-step random delays and packet dropouts
Caballero-Águila, R.
Hermoso-Carazo, Aurora
Linares-Pérez, Josefa
Least-squares linear estimation
Algorithm
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.
2014-11-05T12:59:33Z
2014-11-05T12:59:33Z
2010
info:eu-repo/semantics/article
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]
1024-123X
http://hdl.handle.net/10481/33538
10.1155/2010/258065
eng
info:eu-repo/semantics/openAccess
Hindawi Publishing Corporation