Maneuvering target tracking with high-order correlated noise - a multirate Kalman filtering approach

A multirate Kalman filtering algorithm for target tracking with high-order correlated noise is proposed. The measurement signal is first split into subbands using a filter bank. Then, the correlated noise in each subband is modeled using a first-order AR process and the AR parameters are identified online. Finally, a multirate Kalman reconstruction filter is used to obtain the state estimate. This method can be directly incorporated into the IMM algorithm, resulting in an effective tracking scheme. Simulations show that the new multirate processing scheme can significantly improve tracking performance.