Design of RLS Wiener Smoother and Filter from Randomly Delayed Observations in Linear Discrete-Time Stochastic Systems
Автор: Seiichi Nakamori
Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs
Статья в выпуске: 9 Vol. 5, 2013 года.
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This paper presents the new algorithm of the recursive least-squares (RLS) Wiener fixed-point smoother and filter based on the randomly delayed observed values by one sampling time in linear discrete-time wide-sense stationary stochastic systems. The observed value y(k) consists of the observed value y¯(k-1) with the probability p(k) and of y¯(k) with the probability 1-p(k). It is assumed that the delayed measurements are characterized by Bernoulli random variables. The observation y¯(k) is given as the sum of the signal z(k)=Hx(k) and the white observation noise v(k). The RLS Wiener estimators use the following information: (a) the system matrix for the state vector x(k); (b) the observation matrix H (c) the variance of the state vector x(k); (d) the delayed probability p(k); (e) the variance of white observation noise v(k); (f) the input noise variance of the state equation for the augmented vector v¯(k) related with the observation noise.
Discrete-Time Stochastic Systems, RLS Wiener Filter, RLS Wiener Fixed-Point Smoother, Randomly Delayed Observations, Covariance Information
Короткий адрес: https://sciup.org/15011956
IDR: 15011956
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