Modeling and estimation of a class of dynamic multiscale system subject to colored noise

• Published in: 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing Vol. 2; pp. ii - 913
• Main Authors: Peiling Cui, Quan Pan, Hongcai Zhang, Junhong Li
• Format: Conference Proceeding
• Language: English
• Published: Piscataway, N.J IEEE 2004
• Subjects: Applied sciences; Colored noise; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Frequency; Information, signal and communications theory; Noise measurement; Partial differential equations; Power system modeling; Sampling methods; Sensor systems; Signal and communications theory; Signal resolution; Signal, noise; State estimation; State-space methods; Telecommunications and information theory; Monte Carlo method; Kalman filter; Color noise; State space method; Dynamical system; Haar function; Noise measurement; Algorithm; Modeling; Equations of state; Wavelet transformation; Multiscale method; Signal processing; Numerical simulation
• ISBN: 9780780384842; 0780384849
• ISSN: 1520-6149
• DOI: 10.1109/ICASSP.2004.1326407

In this paper, the modeling and estimation of a class of dynamic multiscale system, subject to colored state equation noise and measurement equation noise, is proposed. The state equation is whitened firstly and then the measurement equation. The state space projection equation is used to link the scales, then a new system model is built. The new model is in a form suitable for the application of the Kalman filter equations. A Haar-wavelet-based model and estimation algorithm are given. Monte Carlo simulation results demonstrate that the proposed algorithm is effective and powerful in this kind of multiscale estimation problem.