【作者】 赵倩；

【导师】 刘国海；

【作者基本信息】 江苏大学 ， 控制理论与控制工程， 2009， 硕士

【摘要】 车载导航及定位是在全球卫星定位系统(GPS,Global Positioning System)的基础上发展起来的一门新型技术。近年来,随着科学技术的发展,GPS导航和定位技术已向高精度、高动态的方向发展。但是由于GPS定位包含许多误差源,尤其是测量随机误差和卫星的几何位置误差,使定位精度受到影响。利用传统的方法很难消除。而GPS动态滤波是消除GPS定位随机误差的重要方法,即利用特定的滤波方法消除各种随机误差,从而提高GPS导航定位精度。本文首先分析了GPS定位的基本原理、误差来源及系统构成。重点介绍了影响GPS定位精度两个关键的客观因素:定位信号与接收机,并对GPS信号进行了仿真。详细介绍了差分GPS技术的原理、分类及应用优势。其次,由于GPS静态定位和动态定轨中,GPS数据预处理质量的好坏都直接影响着GPS定位精度,因此本文以GPS数据预处理理论和计算机应用技术为基础,巧妙运用了统计分析软件SPSS,重点对GPS数据预处理进行了应用研究;并且探讨了观测数据粗差检测与剔除的新方法。接着,本文将一种新的非线性滤波方法——中心差分卡尔曼滤波(CDKF)用于车辆导航中,进行了仿真试验研究。和普遍采用的EKF方法相比,CDKF方法不仅提高了定位的精度和稳定性;而且不需要模型的具体解析形式,避免了复杂的Jacobian矩阵的计算,算法更简单,也更加易于实现。而且较目前存在的另一种非线性滤波算法UKF,也有一定的优势。仿真实验结果进一步表明CDKF方法明显优于EKF、UKF方法,是车辆导航中一种更理想的非线性滤波方法,真正实现了车辆低成本、高精度的实时定位。最后,拟定了GPS动态试验的新方案,进行了实际跑车实验,通过匀速运动车辆的DGPS及GPS的滤波对比试验,验证了新的中心差分卡尔曼滤波算法在处理动态估计问题上的实用性。仿真实验和实际车辆动态导航试验均表明:比起传统卡尔曼滤波算法,中心差分卡尔曼滤波法精度及稳定性更好,实用性更强。更多还原

【Abstract】 The automobile guidance and the localization are developed in the whole world satellite positioning system(GPS,Global Positioning System)foundation as a new technology.In these years,with the development of navigation and orientation,people have higher demands to the orientation precision.It is necessity to improve on the filtering means.First of all,the basic principle,system structure and error sources of GPS positioning are analyzed.The discussion focuses on two important objective factors that influence positioning accuracy:localization signal and the receiver Furthermore, GPS signal is simulated.The principle,variety and advantage of DGPS are analyzed particularly.Next,the quality of GPS data Pre-Proeessing is the key to improve GPS positioning precision in either GPS static positioning or dynamic positioning.So based on the data processing theories for GPS and computer technologies,the data pre-processing are researched mainly in this paper,via a statistics software-SPSS skillfully.And the new method that the gross error of measuring data is detected and eliminated is discussed.In addition,The Central Difference Filter(CDKF) as a new nonlinear filtering method is applied to the nonlinear state estimation of the vehicle navigation systems. Compared with the EKF method,the CDKF method not only improves the location precision and algorithm stability greatly,but also avoids the computing burden of Jacobian matrices.This data fusion algorithm based on CDKF is easy to realize,and meets the requirements of low-cost and high precision.Moreover CDKF compared to UKF has the superiority.The results of experimentation show that the CDKF is superior to the EKF,UKF and it is a more ideal nonlinear filtering method for the vehicle navigation.Finally,a vehicle simulation experiment is made,after the scheme about dynamic test of GPS is discussed,and practicality of the new kalman filter is verified, considering filtering comparison between DGPS and GPS of the uniform motion vehicle.Results of the simulation experiment and test show that the improved CDKF has not only better accuracy and stability,but higher practicality than traditional kalman filter.更多还原