S. Nakamori, R. Caballero-águila, A. Hermoso-Carazo and J. Linares-Pérez, “Linear Recursive Discrete-Time Estimators Using Covariance Information under Uncertain Observations,” Signal Processing, Vol. 83, No. 7, 2003, pp. 1553-1559. doi:10.1016/S0165-1684(03)00056-2

has been cited by the following article:

• TITLE: RLS Wiener Predictor with Uncertain Observations in Linear Discrete-Time Stochastic Systems

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

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

  JOURNAL NAME: Journal of Signal and Information Processing, Vol.2 No.3, August 31, 2011

  ABSTRACT: 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)=Hx(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).