A New Multiscale Associated Filter with multisensors for Dynamic Systems

This paper proposes a new multiscale associated filter, named as the multiscale sequential block Kalman filter (MSBKF). It processes a dynamic system with multisensor of different sampling rate. By combing the wavelet transform and Kalman filtering, the MSBKF is real time, recursive, optimal and has the capability of the multiscale analysis; thus, it overcomes the shortcomings of the existing hybrid wavelet-Kalman filters. New blocked state and measurement systems are presented and reformulated in the multiscale domain by the q-band orthogonal wavelet transformation. New filters are compared with the conventional point Kalman filter via theoretical and numerical analyses