A class of wavelet transform based distributed information consensus filtering algorithm is studied. Firstly, the Haar wavelet transform is applied to establish the systems models of the target state and its observations at different coarser scales. Then, based on the above models, the consensus-based distributed information filtering is proceed at different coarser scales. Finally, the inverse Haar wavelet transform is applied to reconstruct the estimation of target state at the finest scale(initial scale) by using estimations at different coarser scales. Simulation results show that the proposed algorithm can effectively improve the computation efficiency of the consensus-based distributed information filtering algorithm.