本文將基于模型的動(dòng)態(tài)系統(tǒng)分析方法與具有統(tǒng)計(jì)特性的多尺度信號(hào)變換方法相結(jié)合,基于某一尺度上給定的單傳感器單模型動(dòng)態(tài)系統(tǒng),建立起一個(gè)新的多尺度動(dòng)態(tài)模型. 基于建立的多尺度模型和標(biāo)準(zhǔn)Kalman 濾波,提出一個(gè)能同時(shí)對(duì)隨機(jī)信號(hào)進(jìn)行多尺度分解與多尺度估計(jì)的新算法. 獲得比僅在原始尺度上進(jìn)行Kalman 濾波好的處理效果,應(yīng)用Monte Carlo 仿真驗(yàn)證了算法的有效性.
關(guān)鍵詞: 　多尺度估計(jì); 動(dòng)態(tài)系統(tǒng); 隨機(jī)信號(hào); 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