Application of multi-sensor information fusion based on improved particle swarm optimization in unmanned system path planning

被引：2
作者：
Shi G. [1 ]
He Y. [1 ]
Li B. [1 ]
Luo Q. [1 ]
机构：
[1] School of Electrical Engineering and Automation, Hefei University of Technology, Hefei
来源：
He, Yigang (694698336@qq.com) | 1600年 / Kassel University Press GmbH卷 / 13期
基金：
中国国家自然科学基金;
关键词：
Information fusion; Intelligent vehicle; Navigation; Particle filter;
D O I：
10.3991/ijoe.v13i08.7199
中图分类号：
学科分类号：
摘要：
Intelligent vehicle driving performance is safe and stable, which can significantly improve the efficiency of road traffic and reduce energy consumption, and intelligent vehicle is also the development direction of modern transport. Its core technology is intelligent environment perception module, by using a variety of sensors on the car in which the surrounding environment for data collection, processing module to provide effective control for the basis. In this paper, a new SINS / CNS / GPS integrated navigation observation equation is proposed, and a new federated data fusion structure is designed for the integrated navigation system. The particle filter is used to fuse the multi-source data of the federated filter subsystem, thus eliminating the limitations of the classical Kalman filter. The traditional Kalman filter structure and the federal particle filter mechanism are designed. The comparison shows that the proposed algorithm is effective in the information fusion of the integrated navigation system, and the filtering effect is superior to the traditional filtering method.
引用
收藏
页码：88 / 105
页数：17
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