Adaptive State Estimation for a System With Unknown Input and Measurement Bias

An adaptive state estimator for passive underwater tracking of maneuvering targets is developed. The state estimator is designed specifically for a system containing independent unknown or randomly switching input and measurement biases. In modeling the stochastic system, it is assumed that the bias sequence dynamics for both input and measurement can be modeled by a semi-Markov process. By incorporating the semi-Markovian concept into a Bayesian estimation technique, an estimator consisting of a bank of parallel adaptively weighted Kalman filters has been developed. Despite the large and randomly varying biases, the proposed estimator provides an accurate estimate of the system states.