The Multi-scale Distributed Algorithm for Solving Inverse Problem with Multiple Observation Sources

In this paper, a multiscale distributed hierarchical algorithm is developed to solve the computational complexity in inverse problem with multiple observation sources.Firstly, algorithm implements wavelet transform respeetively on the object signal data obtained from multiple observation processes. Secondty, the wavelet transform coefficients are estimated about object signal using the data from each sensor. Thirdty，all local estimates are efficiently fused based on the information provided by relative error covariance matrix，in order to get a global-information-based estimate of the wavelet transform coefficients with the object signal. Fourthty，the inverse wavelet transform is performed on the scaling coefficients at the coarsest scale and the wavelet coefficients at all scales to obtain the global-information-based estimator. Finally, the performance of the algorithm is evaluated with respect to the RECM-based criterion. It is comcluded that the distributed hierarchical fusion algorithm can not only result in an estimator comparable to that of the method of using central fusion algorithm with relatively light computational load, but also enhance the practicability of new algorithm.