Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (5): 1318-1324.DOI: 10.11772/j.issn.1001-9081.2018092020

• Artificial intelligence • Previous Articles     Next Articles

Multiple extended target tracking algorithm for nonliear system

HAN Yulan1, HAN Chongzhao2   

  1. 1. School of Physics and Electronic-Electrical Engineering, Ningxia University, Yinchuan Ningxia 750021, China;
    2. School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an Shaanxi 710049, China
  • Received:2018-10-08 Revised:2018-12-23 Online:2019-05-14 Published:2019-05-10
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61863030, 61650304), the Natural Science Foundation of Ningxia (NZ16004), the Doctoral Starting Funds of Ningxia University (BQD2015007).


韩玉兰1, 韩崇昭2   

  1. 1. 宁夏大学 物理与电子电气工程学院, 银川 750021;
    2. 西安交通大学 电子与信息工程学院, 西安 710049
  • 通讯作者: 韩玉兰
  • 作者简介:韩玉兰(1982-),女,河南新乡人,副教授,博士,主要研究方向:目标跟踪、多源信息融合、信号处理、压缩技术;韩崇昭(1943-),男,陕西乾县人,教授,硕士,主要研究方向:多传感信息融合、随机控制与自适应控制、决策理论与决策支持系统、非线性频谱分析。
  • 基金资助:

Abstract: Most of current extended target tracking algorithms assume that its system is linear Gaussian system. To track multiple extended targets for nonlinear Gaussian system, an multiple extended target tracking algorithm using particle filter to jointly estimate target state and association hypothesis was proposed. Firstly, the idea of joint estimation of the multiple extended target state and association hypothesis was proposed, which avoided mutual constraints in estimating target state and data association. Then, based on extended target state evolution model and measurement model, a joint proposal distribution function for multiple extended target and association hypothesis was established, and the Bayesian framework for the joint estimation was implemented by particle filtering. Finally, to avoid the dimension disaster problem in the implementation of the particle filter, the generation and evolution of the multiple extended target combined state particles were decomposed into that of the individual target state particles, and the particle set of each target was resampled according to the weight association with it, so that each target retained the particles with better state estimation while suppressing the poor part of target state estimation. Simulation results show that, in comparison with the Gaussian-mixture implementation of extended target probability hypothesis density filter and the sequential Monte Carlo implementation of that, the estimation accuracy of the target state is improved, and the Jaccard distance of shape estimation is reduced by approximately 30% and 20% respectively. The proposed algorithm is more suitable for multiple extended target tracking of the nonlinear system.

Key words: extended target tracking, nonlinear system, Bayesian framework, joint estimation, particle filter, proposal distribution function

摘要: 目前扩展目标跟踪算法大都假设其系统为线性高斯系统,针对非线性系统的多扩展目标跟踪问题,提出了采用粒子滤波技术对目标状态和关联假设进行联合估计的多扩展目标跟踪算法。首先,提出了将多扩展目标状态和关联假设进行联合估计的思想,解决了在估计目标状态和数据关联时相互牵制的问题;其次,根据扩展目标演化模型、量测模型建立多扩展目标状态和关联假设的联合建议分布函数,并利用粒子滤波技术实现联合估计的Bayes框架;最后,为解决直接采用粒子滤波实现时存在的维数灾难问题,将目标联合状态粒子的产生和演化分解为各个目标状态粒子的产生和演化,对每个目标的粒子集根据与其相关的权重单独进行重抽样,这样在抑制目标状态估计较差部分的同时使每个目标都保留了对其状态估计较好的粒子。仿真实验结果表明,与扩展目标概率假设密度滤波器的高斯混合实现方式和序贯蒙特卡洛实现方式相比,所提算法的状态估计精度较高,形状估计的Jaccard距离分别降低了30%、20%左右,更适合于非线性系统的多扩展目标跟踪。

关键词: 扩展目标跟踪, 非线性系统, Bayes框架, 联合估计, 粒子滤波, 建议分布函数

CLC Number: