近年来，随着人们对基于位置服务的需求越来越强烈，作为日常生活中使用最广 泛的智能设备，基于智能手机的定位服务越来越多的受到了人们的关注。就目前而言， 使用基于智能手机的 GNSS 芯片进行定位，在开阔环境下可以满足行人的日常定位 需求，但是当受到建筑物或树荫遮挡时，卫星信号损失严重，对于室内场景更是如此。 基于这一事实，本文提出了用于行人定位的 GNSS/PDR 组合定位优化方法，拟通过 二者相结合，实现复杂场景下的精确定位。本文的具体工作内容包括： 1.提出了 GNSS/PDR 组合定位优化方法。首先通过 GNSS 多普勒测速获得行人 的位置改变量，然后根据当前速度的大小推断航向角误差是否在可接受的范围内，通 过得到的位置改变量和航向角这两个信息，使用卡尔曼滤波算法计算 PDR 步长误差 和航向误差，以此进行误差校正，得到行人定位的最终结果。 2.设计了通过三轴加速度方向进行特殊步态（侧移，后退）的识别方法。首先使 用通过智能手机惯性、磁传感器采集的数据进行融合来解算手机的姿态，获取手机的 姿态矩阵。然后利用姿态矩阵将加速度数据从载体坐标系转换到导航坐标系，根据加 速度数据三轴的方向和航向角的变化量来判断行人当前所作的动作是否是特殊的步 态，避免在特殊步态下 PDR 误检测的发生。 3.改进了标准单点定位算法。首先对原始观测数据进行数据预处理，剔除不良的 观测数据，然后使用多普勒测速获得较高精度的位置变化量，通过卡尔曼滤波法将位 置改变量和位置先验信息进行融合，使用多普勒观测值在位置域对定位结果进行了平 滑处理。 4.改善了 PDR 过程中各环节误差对定位结果的影响。首先使用峰值谷值时间阈 值结合的方式进行步数检测，以此来减少加速度数据中伪峰值对步数检测的影响。其 次使用混合步长估计模型完成行人步长的估计，相对于单一模型来讲，增加了步长估 计模型的稳健性。最后，使用改进互补滤波的方法对陀螺仪累积漂移误差进行处理， 以减少误差对姿态解算精度的影响。 最后，设计了 PDR 室内定位实验，验证了改进后的方法与传统 PDR 定位方法相 比，定位的准确性更高。同时，也设计了组合定位的实验，验证了本文所提的 GNSS/PDR 组合定位方法具有较高的定位精度，水平误差在 1-3 米的范围内，满足行 人对日常定位的基本需求。

In recent years, with the increasing demand for location-based services, as the most widely used smart devices in daily life, location-based services based on smartphones have attracted more and more attention. At present, the use of smartphone-based GNSS chips for positioning can meet the daily positioning needs of pedestrians in open environments, but when blocked by buildings or shade, the satellite signal loss is serious, especially for indoor scenes. Based on this fact, this paper proposes a combined GNSS/PDR positioning optimization method for pedestrian positioning, and intends to achieve precise positioning in complex scenes by combining the two. The specific work of this paper includes: 1. A combined GNSS/PDR positioning optimization method is proposed. First, the pedestrian's position change is obtained through GNSS Doppler speed measurement, and then it is inferred whether the heading angle error is within an acceptable range according to the current speed. Through the obtained position change and heading angle, the Kalman filter is used. The algorithm calculates the PDR step error and heading error, and then performs error correction to obtain the final result of pedestrian positioning. 2. This paper proposes a method for recognizing special gaits (side shifts, back-ups). First, use the data collected by the inertial and magnetic sensors of the smartphone to fuse to calculate the attitude of the mobile phone, and obtain the attitude matrix of the mobile phone. Then use the attitude matrix to convert the acceleration data from the carrier coordinate system to the navigation coordinate system, and judge whether the pedestrian's current action is a special gait according to the direction of the three axes of the acceleration data and the change of the heading angle, so as to avoid the special gait. Occurrence of PDR false detection. 3. The standard single point positioning algorithm has been improved. Firstly, data preprocessing is performed on the original observation data, and the bad observation data is eliminated, and then the Doppler velocimetry is used to obtain a higher-precision position change. The Puller observations smoothed the localization results in the location domain. 4. Improve the influence of the errors of each link in the PDR process on the positioning results. Firstly, step detection is performed using a combination of peak-valley time thresholds as a way to reduce the effect of pseudo-peaks in acceleration data on step detection. Secondly, the mixed step size estimation model is used to complete the estimation of pedestrian step size, which increases the robustness of the step size estimation model compared with the single model. Finally, the improved complementary filtering method is used to reduce the influence of the accumulated drift error on the attitude calculation accuracy. Finally, a PDR indoor positioning experiment is designed to verify that the improved method has higher positioning accuracy than the traditional PDR positioning method. Furthermore, we design a combined positioning experiment, which verifies that the GNSS/PDR combined positioning method proposed in this paper has high positioning accuracy and that the horizontal error is within the range of 1 to 3 meters, which meets the basic positioning needs of pedestrians on a daily basis.

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