Jump detection/estimation using stepwise regression

The detection and estimation of jumps with unknown time and magnitude in specific states of a dynamic process is addressed. Unlike most of the techniques described in the literature, the method can handle multiple jumps within the data window. This allows the use of longer data spans with consequently improved jump estimation. Jumps are treated as bias states, and the innovations and innovation sensitivities from a jump-free filter are used as data for regression. Forward stepwise regression provides the means to systematically search all the jump possibilities. Removal of other bias states from the filter and inclusion in the regression improves the performance of the method. A realistic inertial navigation example with multiple jumps is given to demonstrate the advantages of the technique. The method works best offline using the entire data span, but the performance of the online moving window version is only slightly degraded