| 粒子滤波融合跟踪方法 |
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| 引用本文： | 刘宗礼,刘涛,曹洁.粒子滤波融合跟踪方法[J].科学技术与工程,2009,9(7). |
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| 作者姓名： | 刘宗礼 刘涛 曹洁 |
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| 作者单位： | 兰州理工大学计算机与通信学院,兰州,730050 |
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| 摘 要： | 针对Kalman滤波不能处理多传感器量测信息融合中的非线性问题,提出了一种基于粒子滤波方法的融合跟踪算法.通过对量测方程的非线性分析,利用粒子滤波器计算目标状态估计值,通过线性迭代的方式得到系统的最优估计.仿真结果表明,与采用Kalman滤波的方法相比,该算法具有更高的估计精度和更少的计算量.相比于单传感器,减少了量测信息的模糊性,提高了资源的利用率.
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| 关 键 词： | 粒子滤波 多传感器 数据融合 目标跟踪 |
Fusion Tracking Algorithm Based on Particle Filter |
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| LIU Zong-li,LIU Tao,CAO Jie.Fusion Tracking Algorithm Based on Particle Filter[J].Science Technology and Engineering,2009,9(7). |
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| Authors: | LIU Zong-li LIU Tao CAO Jie |
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| Affiliation: | School of Computer and Communication;Lanzhou University of Technology;Lanzhou 730050;P.R.China |
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| Abstract: | Aiming at the restriction of Kalman filter in dealing with nonlinear problems of measurement information in multi-sensor data fusion,a fusion tracking algorithm based on particle filter is proposed.It uses particle filter to calculate state estimated values by the non-linear analysis of measurement equation,and then the system optimal estimation is obtained in the linear iterative way.The simulation results show that compared with the Kalman filter,the proposed algorithm improves the estimation accuracy and... |
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| Keywords: | particle filter multi-sensor data fusion object tracking |
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