本文將基于模型的動態(tài)系統(tǒng)分析方法與具有統(tǒng)計特性的多尺度信號變換方法相結合,基于某一尺度上給定的單傳感器單模型動態(tài)系統(tǒng),建立起一個新的多尺度動態(tài)模型. 基于建立的多尺度模型和標準Kalman 濾波,提出一個能同時對隨機信號進行多尺度分解與多尺度估計的新算法. 獲得比僅在原始尺度上進行Kalman 濾波好的處理效果,應用Monte Carlo 仿真驗證了算法的有效性.
關鍵詞: 　多尺度估計; 動態(tài)系統(tǒng); 隨機信號; Kalman 濾波; 小波變換
Abstract : 　By combining the model2based analysis for dynamic systems with multiscale signal transformation ,we propose a new multiscale dynamic model based on dynamic system with single model and single sensor at one given scale.By developing Kalman fil2
tering we put froward a new multiscale algorithmfor stochastic signal on the basis of the multiscale dynamic model ,and we can simul2 taneously implement decomposition and estimation for stochastic signal. The estimates obtained by use of the new algorithm is more ac2 curate than the results based on Kalman filtering at the finest scale. A set of Monte Carlo simulations is performed ,and the resluts show the efficiency of our new algorithm.
Key words : 　multiscale estimation ;dynamic system;stochastic signal ;Kalman filtering ;wavelet transform