Fixed-order optimal deconvolution filter with irregular missing data

摘要

In general, the reconstruction performance of the conventional deconvolution filter is deteriorated by the missing data. In this paper, a fixed-order deconvolution filter design method is proposed for the signal reconstruction from received signal with irregular missing data. The missing data model is based on a probabilistic structure. The probability of occurrence of missing data is unknown a prior. In this situation, the deconvolution filter design problem becomes a complicated nonlinear estimation problem. In this study, a design method based on genetic algorithms is proposed to treat the signal reconstruction design problem with irregular missing data. Finally, two examples are given to illustrate the simulation results of the proposed deconvolution filter. The results show that the reconstruction performance is improved significantly if the missing probability is considered in the deconvolution filter design procedure.