本文为美国宾夕法尼亚州立大学（作者：Sneha Kadetotad）的硕士论文，共82页。

近年来，不使用GPS的道路车辆定位问题引起了人们的极大兴趣，并提出了许多解决方案。传统的车辆定位方法分为两类：基于特征向量匹配的全局定位和基于粒子滤波、卡尔曼滤波等技术的局部跟踪。这项工作提出了一种统一的方法，将基于特征的全局搜索鲁棒性与粒子滤波器的局部跟踪能力结合起来。本文利用宾夕法尼亚州I-80州际公路和220号美国公路沥青测量得到的特征向量，证明了车辆广域定位和局部跟踪的计算效率。

The localization of vehicles on roadwayswithout the use of a GPS has been of great interest in recent years and anumber of solutions have been proposed for the same. The localization ofvehicles has traditionally been divided by their solution approaches into twodifferent categories: global localization which uses feature-vector matching,and local tracking which has been dealt with using techniques like Particlefiltering or Kalman Filtering. This effort proposes a unifying approach thatcombines the feature-based robustness of global search with the local trackingcapabilities of a Particle filter. Using feature vectors produced from pitchmeasurements from Interstate I-80 and US Route 220 in Pennsylvania, this workdemonstrates wide area localization of a vehicle with the computationalefficiency of local tracking.

1 引言

2 文献回顾

3 全局定位与局部跟踪

4 基于特征的粒子滤波

5 结果

6 应用与未来工作展望

附录1 MATLAB源码：基于特征的粒子滤波算法

附录2 MATLAB源码：导入数据

附录3 MATLAB源码：在每一次迭代评估粒子参数

附录4 MATLAB源码：重采样

附录5 MATLAB源码：绘图

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