In this paper,with analysis on the dynamic system model based on wavelet transform,a novel data fusion filtering algorithm based on multiscale Kalman filter was proposed.Wavelet transform was very suitable for processing multiscale signals,and it could be used in multiresolution fusion filtering.The approach used the multi-scale characteristic of wavelet transform to decompose the primal forecasting sequence firstly,and performed Kalman filtering estimate on each level,and lastly wavelet reconstruction was applied to integrate the estimate information from neighborhood scale.So the better filtering precision is obtained.The wavelet decomposition of the noised measurements was combined with Kalman filter in every scale and it makes the best of multiresolution data information.Thus,the better filtering results are achieved for the dynamic system with high-level noise in practice.