Multi-Sensor Fusion Tracking Algorithm Based on Augmented Reality System

Abstract

With the rapid development of modern science and technology, multi-sensor information fusion target tracking technology has gradually become an increasingly important topic in the field of computer vision. It is not only of great significance in traffic monitoring, unmanned vehicle systems, human-computer interaction, etc., but also has a wide range of applications in target tracking. Aiming at the problem of multi-sensor multi-target tracking, this article proposes a multi-sensor multi-target tracking algorithm based on random finite set. The basic theory of random sets, random finite set statistics and the probability hypothesis density of random sets are introduced. The distribution function of random sets and the derivation process of set derivatives and set integrals are discussed, and the theoretical framework of information fusion based on random sets is given. The single-sensor single-target specification Bayes modeling method is extended to the multi-sensor multi-target situation, and the significance of the probability hypothesis density is explained, and the multi-sensor multi-target tracking is realized by the probability hypothesis density filtering. The simulation experiment results show that the algorithm has a better tracking effect for the maneuvering target in the nonlinear motion model stage, and it is verified that the unscented Kalman filter has smaller filtering error and higher estimation accuracy than the extended Kalman filter.