Tracking technique for manoeuvring target with correlated measurement noises and unknown parameters

The problem of tracking a manoeuvring target with sequentially correlated measurement noise is considered in the paper. Using Singer´s method to model the manoeuvring target, the correlated measurement noise can be decorrelated by reformulating the measurement equation such that the conventional Kalman filter can be directly applied in this tracking problem. An analytical error analysis for this processing is derived. If some of the parameters are unknown, the conventional innovation correlation method can usually be employed to estimate these parameters adaptively. This method assumes that the measurement noise is white. If the measurement noise is sequentially correlated, this technique is not valid and the parameters can not be estimated with sufficient accuracy to obtain the desired tracking performance. By considering the effect of noise correlation, a modified computationally efficient method known as a multiple-level estimator is presented to improve the performance in estimating the unknown parameters in the presence of correlated measurement noise