Journal of Xidian University ›› 2020, Vol. 47 ›› Issue (5): 137-143.doi: 10.19665/j.issn1001-2400.2020.05.018

• Information and Communications Engineering & Cyberspace Security • Previous Articles     Next Articles

GLMB extended target tracking based on one-step data association

LI Cuiyun(),LI Yang,JI Hongbing,SHI Renzheng   

  1. School of Electronic Engineering, Xidian University, Xi’an 710071, China
  • Received:2019-10-11 Online:2020-10-20 Published:2020-11-06


Due to the inseparability of measurements in neighborhood scenarios, the tracking performance of the traditional extended target tracking algorithm would degrade. In this paper, a new extended target tracking algorithm based on one step data association is proposed to solve the problem. First, the algorithm models the target with a multiplicative noise model. And then, the one step data association method in the Joint Probabilistic Data Association (JPDA) theory is combined with a Generalized Labeled Multi-Bernoulli (GLMB) filter. Simulation results show that the algorithm can track the target in cross and neighborhood scenarios effectively and that it is superior to the traditional extended target tracking algorithms based on measurement partition in estimation accuracy.

Key words: extended target tracking, multiplicative noise model, second-order extended kalman filtering, data association, generalized labeled multi-bernoulli filter

CLC Number: 

  • TN953