基于多GPS数据融合的无人机导航技术研究

摘要GPS导航是当前飞行器导航控制的主要方法，但民用 GPS的导航精度较差，静态点位置漂移较为严重，从而严重影响了无人机飞行的控制精度。而高精度的 GPS传感器价格过高，不适合民用。因此导航中需要多GPS 进行数据融合，在不显著增加成本的前提下，提高飞行器导航精度。本文首先简单介绍了无人机导航系统的发展背景以及国内外应用状况，接着介绍多数据融合技术在导航系统中的基本概念与应用状况。详细论述了 Kalman 滤波理论的基本原理和发展应用，为整篇文章提供了理论基础。并运用 Matlab 软件对实际情况进行分析，建立空间状态模型，将多数据融合技术与滤波理论相结合，分析出最为有效的算法。最后利用无人机进行实物仿真，通过多组经纬度测量的实验数据的分析对比，证实融合算法的有效性。实际的飞行曲线表明了多GPS 数据融合算法可以给无人机导航系统提供更为精准、可靠的定位信息。43121

毕业论文关键词导航精度多GPS 数据融合 Kalman 滤波

Title The study of unmanned aerial vehicle navigationtechnology based on multiple GPS data fusionAbstractNowadays,GPS navigation is the main method of aircraft navigation control.But the navigationaccuracy of civil GPS is less accurate,which seriously affects the UAV flight controlaccuracy.And the high accuracy of the GPS sensor is too expensive,not suitable for civiluse.Therefore,the navigation system needs to have more than one GPS sensor, to carry on thedata fusion, through the algorithm to make up for the lack of cost.First of all, this paper introduces the development background of UAV navigation system and itsapplication at home and abroad,then introduces the basic concepts and application of themultiple data fusion technology in the navigation system.The Kalman filter theory is used as theprocessing of focus,which basic principle and development of the application are discussed indetail,and introduce the theoretical basis for the following article.And the use of Matlab softwarefor the analysis of the actual situation,and establish the space state model,combining thetechniques of multi-data fusion with the filtering theory, analyze the most efficientalgorithm.Through the analysis and comparison of the experimental data measured by themultiple sets of latitude and longitude, the validity of the fusion algorithm is verified.The actualflight curve shows that the multi-sensor data fusion algorithm can provide more accurate andreliable positioning information for the UAV navigation system.

Keywords Navigation accuracy Multi GPS Data fusion The Kalman filter

1绪论1

1.1课题研究的背景及意义1

1.2无人机发展的国内外现状1

1.3导航技术中的数据融合2

1.4本文的主要工作内容3

2多传感器数据融合技术4

2.1数据融合的基本概念4

2.2多传感器数据融合的结构4

2.3多传感器数据融合的方法6

2.4数据融合在导航技术中的应用与发展8

2.5本章小结9

3多传感器数据融合算法仿真10

3.1加权平均法仿真结果与分析10

3.2多点融合算法仿真结果与分析10

3.3一阶滞后滤波算法仿真结果与分析13

3.4本章小结15

4Kalman滤波算法在GPS定位系统中的应用16

4.1原理介绍16

4.2仿真结果与分析17

4.3Kalman滤波与加权平均算法相结合18

4.4本章小结23

5融合算法的实现和验证24

5.1实验基础24

5.2GPS模块的通信协议26

5.3坐标系转换27

5.4无人机导航精度的实验与分析29

5.5本章小结32

结论33

致谢34

参考文献35 基于多GPS数据融合的无人机导航技术研究:http://www.youerw.com/zidonghua/lunwen_43805.html