Extended target probability hypothesis density filter based on cubature Kalman filter


Aiming at the extended target tracking drawback during a non-linear Gaussian system, we proposed an extended target likelihood hypothesis density (EPHD) filter based on the cubature Kalman filter (CKF). To approximate the analytical answer of the extended target tracking, a spherical radial cubature rule was applied to form it attainable to numerically compute multivariate moment integrals in the non-linear Bayesian filter. Cubature points and weights were obtained to approximate the integrals in the method. The new algorithm achieved almost the same filtering accuracy as the Gaussian mixture extended Kalman EPHD (EK-EPHD) filter, when solving tracking problems in such advanced conditions that the Jacobian matrix of a non-linear operate does not exist or is troublesome to unravel. This work provides a new approach for the extended target tracking underneath the non-linear Gaussian system.

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