Author(s)

Seiichi Nakamori, Raquel Caballero-Águila, Aurora Hermoso-Carazo, Josefa Linares-Pérez

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This paper proposes recursive least-squares (RLS) l-step ahead predictor and filtering algorithms with uncertain observations in linear discrete-time stochastic systems. The observation equation is given by y(k)=y(k)z(k)+v(k), z(k)=H_x(k), where {y(k)} is a binary switching sequence with conditional probability. The estimators require the information of the system state-transition matrix Ф, the observation matrix H, the variance K(k,k) of the state vector x(k), the variance R(k) of the observation noise, the probability p(k)=p{y(k)=1} that the signal exists in the uncertain observation equation and the (2,2) element [p(k|j)]_2,2 of the conditional probability of y(k), given y(j).

Estimation Theory, Synthesis of Stochastic Systems, RLS Wiener Predictor, Uncertain Observations, Markov Probability

S. Nakamori, R. Caballero-Águila, A. Hermoso-Carazo and J. Linares-Pérez, "RLS Wiener Predictor with Uncertain Observations in Linear Discrete-Time Stochastic Systems," Journal of Signal and Information Processing, Vol. 2 No. 3, 2011, pp. 152-158. doi: 10.4236/jsip.2011.23019.