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

• 作者: Aurora Hermoso-Carazo Josefa Linares-Pérez Raquel Caballero-águila Seiichi Nakamori
• 来源期刊: 信号与信息处理(英文)
• 学科: 数学
• 关键词: Estimation Theory Synthesis of Stochastic Systems RLS WIENER PREDICTOR Uncertain OBSERVATIONS MARKOV PROBABILITY
• 年，卷（期）: 2011,（3）
• 页码范围: 152-158
• 页数: 7页
• 分类号: O1

摘要

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).