A Multisensor Data Fusion Estimation Algorithm for Vehicle Maneuvering Target Tracking

Zhang Wen-feng; Hu Zhen-tao; Cheng Jian-xing

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A Multisensor Data Fusion Estimation Algorithm for Vehicle Maneuvering Target Tracking

更新时间：2026-05-27

- A Multisensor Data Fusion Estimation Algorithm for Vehicle Maneuvering Target Tracking
- Vol. 26, Issue 1, Pages: 16(2009)
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- Online First：01 January 2009，
  
  Published：2009
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张文峰, 胡振涛, 程建兴. 种车辆机动目标跟踪的多传感器信息融合估计算法[J]. 广东工业大学学报, 2009, 26(1): 16.

Zhang Wen-feng, Hu Zhen-tao, Cheng Jian-xing. A Multisensor Data Fusion Estimation Algorithm for Vehicle Maneuvering Target Tracking[J]. 2009, 26(1): 16.
张文峰, 胡振涛, 程建兴. 种车辆机动目标跟踪的多传感器信息融合估计算法[J]. 广东工业大学学报, 2009, 26(1): 16. DOI：

Zhang Wen-feng, Hu Zhen-tao, Cheng Jian-xing. A Multisensor Data Fusion Estimation Algorithm for Vehicle Maneuvering Target Tracking[J]. 2009, 26(1): 16. DOI：

摘要

为了保证自动高速公路系统对车辆机动目标的实时、精确跟踪

提出了一种车辆机动目标状态的多传感器信息融合估计算法.建立了车辆运动状态的离散时间多模式非线性动态系统模型

利用当前时刻的各个传感器量测数据

结合交互式多模型和扩展卡尔曼滤波器得到各个局部状态估计值和滤波误差协方差阵

并采用动态加权信息融合准则获得更为精确的车辆融合航迹估计值.通过仿真验证表明这种多传感器信息融合估计算法能实时有效地提高车辆机动目标跟踪精度.

Abstract

To solve the vehicle’s tracking problems with real time and precision in automated highway system

a novel state estimation algorithm based on multisensor data fusion is proposed. Firstly

a nonlinear system dynamic model was established for the vehicle motion state with discrete time and multiple models. By utilizing each sensor measurement data in current time and combining interacting multiple models (IMM) with extended kalman filter (EKF)

the local state estimation value and error covariance m...

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references

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孙书利,崔平远. 多传感器标量加权最优信息融合稳态Kalman滤波器[J]. 控制与决策. 2004(02)

周军,王志胜,周凤岐. 基于线性均方估计的数据融合理论[J]. 宇航学报. 2003(04)

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盛三元,王建华. 联合卡尔曼滤波在多传感器信息融合中的应用[J]. 雷达与对抗. 2002(01) 中国图书全文数

何友等著.多传感器信息融合及应用[M]. 电子工业出版社, 2000

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