J4 ›› 2014, Vol. 41 ›› Issue (6): 37-44.doi: 10.3969/j.issn.1001-2400.2014.06.007

• 研究论文 • 上一篇    下一篇



  1. (西安电子科技大学 电子工程学院,陕西 西安  710071)
  • 收稿日期:2013-07-25 出版日期:2014-12-20 发布日期:2015-01-19
  • 通讯作者: 赵雪刚
  • 作者简介:赵雪刚(1987-),男,西安电子科技大学硕士研究生,E-mail: xgzhao1987@163.com.
  • 基金资助:


Interacting multiple model box particle filter with quantitative measurements

ZHAO Xuegang;SONG Liping;JI Hongbing   

  1.  (School of Electronic Engineering, Xidian Univ., Xi'an  710071, China)
  • Received:2013-07-25 Online:2014-12-20 Published:2015-01-19
  • Contact: ZHAO Xuegang



关键词: 交互多模型, 粒子滤波, 箱粒子滤波, 量化量测


In the distributed multi-sensor networks, in order to save the communication bandwidth,  to quantize the point observations obtained by sensors into the interval measurements is required. However, the traditional filtering algorithm can not directly deal with the quantitative measurements. The box particle filter (Box-PF) as a "generalized particle filter" algorithm uses the box particles and the bounded error model to replace the traditional point particles and the error statistical model. Therefore, it is a powerful tool for processing interval measurements. Key advantages of the Box-PF against the standard particle filter (PF) are a smaller particle number,  reduced computational complexity and a fast running speed. Therefore, to cope with the maneuvering target tracking with the quantitative measurements, this paper presents an interacting multiple model box particle filter (IMMBPF) algorithm. Simulation results show that under the condition of quantitative measurements IMMBPF and IMMPF are both able to accurately estimate the states of the maneuvering target. The IMMBPF, however, needs fewer particles, and computes more efficiently.

Key words: interacting multiple model, particle filter, box particle filter, quantitative measurements


  • TN953