Sharif Digital Repository

The track fusion combines individual tracks formed by different sensors. Tracks are usually obtained by Kalman Filter (KF), since it is suitable for real-time application. The KF is an optimal linear estimator when the measurement noise has a Gaussian distribution with known covariance. However, in practice, some of the sensors do not have these properties, and the traditional KF is not an optimal estimator. In this paper, a novel adaptive Kalman filter (NAKF) is proposed. In this approach, the measurement noise covariance is adjusted by using an introduced simple mathematical function of one variable, called the degree of matching (DoM), where it is defined on the basis of covariance...