S. S. Xiong and Z. Y. Zhou, “Neural Filtering of Colored Noise Based on Kalman Filter Structure,” IEEE Transactions on Instrumentation and Measurement, Vol. 52, No. 3, 2003, pp. 742-747. doi:10.1109/TIM.2003.814669

has been cited by the following article:

• TITLE: Design of RLS Wiener Smoother and Filter for Colored Observation Noise in Linear Discrete-Time Stochastic Systems

  AUTHORS: Seiichi Nakamori

  KEYWORDS: Discrete-Time Stochastic System; RLS Wiener Filte; RLS Wiener Fixed-Point Smoother; Colored Observation Noise; Covariance Information

  JOURNAL NAME: Journal of Signal and Information Processing, Vol.3 No.3, August 31, 2012

  ABSTRACT: Almost estimators are designed for the white observation noise. In the estimation problems, rather than the white observation noise, there might be actual cases where the observation noise is modeled by the colored noise process. This paper examines to design a new estimation technique of recursive least-squares (RLS) Wiener fixed-point smoother and filter for colored observation noise in linear discrete-time wide-sense stationary stochastic systems. The observation y(k) is given as the sum of the signal z(k)=Hx(k) and the colored observation noise vc(k). The RLS Wiener estimators explicitly require the following information: 1) the system matrix for the state vector x(k); 2) the observation matrix H; 3) the variance of the state vector x(k); 4) the system matrix for the colored observation noise vc(k); 5) the variance of the colored observation noise; 6) the input noise variance in the state equation for the colored observation noise.