Detection of jumps in mean and adaptive filtering

One possible approach for the analysis of nonstationary signals (segmentation, identification, filtering...) consists in the joint use of a suitable estimation algorithm together with a detector of sudden changes in a convenient statistical model, and sequential detectors of jump in mean are of particular interest in this framework. Three types of such detectors are investigated here, and some new algorithms are presented together with a comparative study based upon application to real data (geographysical signals). Finally, the updating scheme for the filter estimates after detection of a jump, is discussed.