Maneuvering target tracking algorithm based on IMM multi-sensor sequential particle filtering

LU Xin-dong; HU Zhen-tao; LIU Xian-xing; JIN Yong

doi:10.13873/j.1000-97872012.04.027

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Maneuvering target tracking algorithm based on IMM multi-sensor sequential particle filtering

更新时间：2025-09-11

- Maneuvering target tracking algorithm based on IMM multi-sensor sequential particle filtering
- Vol. 31, Issue 4, Pages: 130-132+136(2012)
- 作者机构：
  
  河南大学计算机与信息工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872012.04.027
  CLC： TP391.41
- Published：2012
- 稿件说明：
移动端阅览
[1]陆新东,胡振涛,刘先省,金勇.基于IMM多传感器顺序粒子滤波跟踪机动目标算法[J].传感器与微系统,2012,31(04):130-132+136.

LU Xin-dong, HU Zhen-tao, LIU Xian-xing, et al. Maneuvering target tracking algorithm based on IMM multi-sensor sequential particle filtering[J]. 2012, 31(4): 130-132+136.
[1]陆新东,胡振涛,刘先省,金勇.基于IMM多传感器顺序粒子滤波跟踪机动目标算法[J].传感器与微系统,2012,31(04):130-132+136. DOI： 10.13873/j.1000-97872012.04.027.

LU Xin-dong, HU Zhen-tao, LIU Xian-xing, et al. Maneuvering target tracking algorithm based on IMM multi-sensor sequential particle filtering[J]. 2012, 31(4): 130-132+136. DOI： 10.13873/j.1000-97872012.04.027.

摘要

针对交互式多模型粒子滤波在跟踪机动目标时精度受限问题

提出一种基于交互式多模型（IMM）的多传感器顺序粒子滤波算法。采用IMM机制实现目标运动模式的确认;在合理利用单传感器量测和多传感器量测中冗余和互补信息的基础上

引入顺序重抽样方法改善粒子分布

并将改善后的粒子应用于IMM粒子滤波算法框架。仿真实验结果表明:新算法能够估计出强机动目标状态

且精度明显优于标准IMM粒子滤波算法。

Abstract

Aiming at the precision of the interacting multiple models（IMM） with particle filtering is restricted in tracking maneuvering target

a new IMM which adopts the multi-sensor sequential particle filtering algorithm is proposed.In the novel algorithm

the IMM mechanism is used to confirm the motion pattern of target.Based on utilization of the redundancy and complementary measurement information from the single sensor and multi-sensor

a sequential re-sampling method is used to optimize the distribution of particles and integrated into the IMM with particle filtering.Simulation results show that the new algorithm can estimate the state of the maneuvering target well and is superior to the standard IMM with particle filtering algorithm in the precision.

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references

基于粒子优化的多模型粒子滤波算法 [J]. 刘先省,胡振涛,金勇,杨一平. 电子学报 . 2010(02)

将粒子残差一致性度量的滤波算法用于纯方位被动跟踪 [J]. 胡振涛,潘泉,杨峰. 红外与毫米波学报 . 2010(01)

基于IMM-PF的分布式估计融合算法 [J]. 彭志专,冯金富,钟咏兵,伍友利,梁晓龙. 控制与决策 . 2008(07)

基于粒子滤波的分布式故障诊断 [J]. 刘喜梅,魏婉韵,于飞. 传感器与微系统 . 2008(03)

改进的交互式多模型粒子滤波跟踪算法 [J]. 刘贵喜,高恩克,范春宇. 电子与信息学报 . 2007(12)

多传感器顺序粒子滤波算法 [J]. 熊伟,何友,张晶炜. 电子学报 . 2005(06)

雷达数据处理及应用[M]. 电子工业出版社 , 何友[等]编著, 2006

Performance comparison of EKF and particle filtering methods for maneuvering targets [J] . Digital Signal Processing . 2006 (4)

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