Shape-driven multiple extended target tracking and classification based on B-Spline and PHD filter

Abstract

Gaussian inverse Wishart probability hypothesis density (GIW-PHD) filter has been considered a promising algorithm for tracking an unknown number of multiple extended targets (MET) with ellipsoidal shapes. However, when the MET are close to one another with irregularly varying shapes, the tracking accuracy will degrade seriously due to the incorrect measurement partition. To address the problem, we propose a new multiple extended target tracking and classification algorithm based on the shape driven strategy under the framework of PHD. First, the B-spline curve technique is employed to estimate the irregular MET shapes, and then the shape features are extracted for improving the measurement partition and state update for the closely spaced MET. Finally, the MET are classified according to the estimated shape information and the Gaussian mixture implementation of the proposed algorithm is derived and presented in this work. Experimental results show that the proposed technique has a better tracking performance than the existing GIW-PHD for the closely spaced MET with irregular shapes.