Multi-feature weighted nearest neighbor data association and tracking algorithm based on Kalman filter

Abstract

Aiming at the problems of low accuracy and missing association in traditional nearest neighbor data association algorithm , a multi-feature weighted nearest neighbor association algorithm was proposed. A similarity function was defined according to the obstacle data obtained by the intelligent vehicle environment perception system, and a method was proposed to calculate the effective correlation degree based on the life cycle, so as to determine whether the objects are related. Based on Kalman filter, the associated target was updated iteratively to realize the tracking of the target. The tracking trajectory of stationary target, low-speed moving target without interaction and low-speed moving target with interaction were compared through experiments. Experimental results show that compared with the conventional nearest neighbor data association algorithm, the improved algorithm proposed in this paper can realize the accurate and continuous associated tracking of low-speed moving targets, and there will be no the phenomenon of target loss or position mutation. With high effectiveness and practicability, the interaction and occlusion of tracking targets have less effect on the tracking performance. The research results may provide reference for the target tracking design of intelligent vehicles.

Keywords

• sensor technology; intelligent vehicle; data correlation; target tracking; kalman filter; nearest neighbor