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The goal of the Doppler and Bearing Tracking (DBT) as a kind of passive target tracking problem is to estimate the position and velocity of the target using its transmitted signal. The main problem of this kind of target tracking is nonlinearity of the measurement equations. In order to solve this problem, different approaches have been reported in the literature, such as extended Kalman filter. However, bias and dependence on the initial conditions are the key shortcomings of such filters. In this paper, first, a novel technique is proposed to provide an appropriate initial condition for the filter. Then, inspired by the modified covariance extended Kalman filter, a new adaptive extended Kalman filter is introduced. Here, the measurement and the process noise covariances are updated simultaneously for reducing the bias effects. Finally, the performance of the proposed filter is compared with the standard extended Kalman filter, adaptive extended Kalman filter and unscented Kalman filter. Results show the good performance of the proposed filter in the problem under study.

Doppler and Bearing Tracking using Adaptive Modified Covariance Extended Kalman Filter