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研究一类基于小波变换的分布式信息一致滤波算法.首先,利用Haar小波变换建立目标状态及其观测在不同粗尺度下的系统模型;然后,基于该模型,在不同粗尺度上分别进行分布式信息一致滤波估计;最后,针对不同粗尺度估计,通过Haar小波逆变换重构最细尺度(初始尺度)目标状态的估计.仿真结果表明,所提出的算法可以有效提高分布式信息一致滤波算法的计算效率.
This paper studies a class of distributed information uniform filtering algorithm based on wavelet transform. Firstly, Haar wavelet transform is used to establish the target state and its observed system model under different coarse scales. Then, based on the model, Finally, according to different coarse-scale estimation, the estimation of target state at the finest scale (initial scale) is reconstructed by Haar wavelet inverse transform.The simulation results show that the proposed algorithm can effectively improve the performance of the distributed information uniform filtering algorithm Computational efficiency.