Doppler and bearing tracking using fuzzy adaptive unscented Kalman filter

The topic of Doppler and Bearing Tracking (DBT) problem is to achieve a target trajectory using the Doppler and Bearing measurements. The difficulty of DBT problem comes from the nonlinearity terms exposed in the measurement equations. Several techniques were studied to deal with this topic, such as the unscented Kalman filter. Nevertheless, the performance of the filter depends directly on the prior knowledge, involving the accurate model, sufficient information of the noise distribution and the suitable initialization. To address these problems, in this paper, a new adaptive factor together with a fuzzy logic system is proposed for online adjusting the process and the measurement noise covariance matrices simultaneously. In the core of the proposed algorithm, the fault detection procedure is also adopted to reduce the computational time. The theoretical developments are investigated by simulations, which indicate the effectiveness of the proposed filter in DBT problem.