State estimation with biased observations

State estimation is difficult when the system has multiple modes of operati on. Modal transitions create discontinuities in the reference point for the local state variables. The uncertain reference point increases the ambigui ty in the state measurement. This paper presents an estimation algorithm th at can be used in multimodal applications. The algorithm is shown to be sup erior to the Kalman filter when the state measurement is contaminated with a mode dependent offset. Despite the uncertain reference point in the obser vation, good estimates of the underlying entire state processes can be gene rated.