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本文将基于模型的动态系统分析方法与具有统计特性的多尺度信号变换方法相结合 ,基于某一尺度上给定的单传感器单模型动态系统 ,建立起一个新的多尺度动态模型 .基于建立的多尺度模型和标准Kalman滤波 ,提出一个能同时对随机信号进行多尺度分解与多尺度估计的新算法 .获得比仅在原始尺度上进行Kalman滤波好的处理效果 ,应用MonteCarlo仿真验证了算法的有效性 .
In this paper, a new multi-scale dynamic model is established based on the single-sensor single-model dynamical system given on a certain scale by combining the model-based dynamic system analysis method with the multi-scale signal transformation method with statistical properties.Based on the established Multi-scale model and standard Kalman filter, a new algorithm for multi-scale decomposition and multi-scale estimation of stochastic signals is proposed. The Kalman filtering is more effective than the original scale only. Monte Carlo simulation shows that the proposed algorithm is effective Sex.