State estimation with biased observations

State estimation is difficult when the system has multiple modes of operation. Modal transitions create discontinuities in the reference point for the local state variables. The uncertain reference point increases the ambiguity in the state measurement. The paper presents an estimation algorithm that can be used in multimodal applications. The algorithm is shown to be superior to the Kalman filter when the state measurement is contaminated with a mode dependent offset. Despite the uncertain reference point in the observation, good estimates of the underlying entire state processes can be generated.