| 扩展卡尔曼滤波的安卓手机定位算法研究 |
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| 引用本文： | 袁阳,周汝雁,张云.扩展卡尔曼滤波的安卓手机定位算法研究[J].全球定位系统,2020,45(2):105-111. |
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| 作者姓名： | 袁阳 周汝雁 张云 |
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| 作者单位： | 1.上海海洋大学 信息学院,上海 201306 |
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| 基金项目： | 国家自然科学基金(41871325);上海市青年科技启明星计划(17QB1403800)。 |
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| 摘 要： | 随着手机定位的应用越来越多,目前市场中许多APP(Application)都会用到定位功能.但多数APP使用传统的定位算法,不能满足人们实时获取高精度地理位置信息的需求.现阶段对于手机的全球定位系统(GPS)芯片原始数据定位方法的研究较少,因此本文主要对利用手机GPS原始数据定位的可行性及定位算法进行了研究.利用Android 7.0系统提供的应用程序接口获取GPS芯片的原始数据参数,根据手机实用场景的速度特征,分别设计并实现了针对于静态场景的静态卡尔曼滤波和针对低速场景的动态卡尔曼滤波定位算法.通过静态实验以及电动车实验和步行实验的结果表明:与传统的定位算法相比,本文设计的静态卡尔曼滤波和动态卡尔曼滤波定位算法拥有更好的定位结果,更加接近实际行走路线,证明了利用手机GPS原始数据定位的可行性,同时也证明了设计的卡尔曼滤波算法可以提高定位精度,论文的研究结果为实现静态与动态的高精度手机定位算法提供了理论依据.
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| 关 键 词： | 手机APP定位 最小二乘法 静态卡尔曼滤波 动态卡尔曼滤波 |
Research on the location algorithm of android mobile phone based on extended Kalman filter |
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| YUAN Yang,ZHOU Ruyan,ZHANG Yun.Research on the location algorithm of android mobile phone based on extended Kalman filter[J].Gnss World of China,2020,45(2):105-111. |
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| Authors: | YUAN Yang ZHOU Ruyan ZHANG Yun |
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| Institution: | 1.College of Information Technology,Shanghai Ocean University,Shanghai 201306 China2.China Key Laboratory of Fisheries Information, Ministry of Agriculture, Shanghai Ocean University,Shanghai 201306,China |
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| Abstract: | At present, there are more and more applications for mobile phone positioning, and many APPs (Applications) in the market will use the positioning function. However, most APPs use traditional positioning algorithms and can not meet the needs of people to obtain high-precision geographic location information in real time. At present, there is less research on the positioning method of the mobile phone's GPS chip raw data. Therefore, this paper mainly studies the feasibility and positioning algorithm of using the mobile phone's GPS raw data. Using the application program interface provided by the Android 7.0 system to obtain the raw data parameters of the GPS chip, according to the speed characteristics of the practical scene of the mobile phone, the static Kalman filtering for static scenes and the dynamic Kalman filtering positioning algorithm for low-speed scenes were designed and implemented respectively. Through static experiments as well as electric vehicle experiments and walking experiments, the experimental results show that compared with traditional positioning algorithms, the static Kalman filtering and dynamic Kalman filtering positioning algorithms designed in this paper have better positioning results and are closer to the actual walking route. It proves the feasibility of using mobile phone GPS raw data to locate, and also proves that the designed Kalman filter algorithm can improve the positioning accuracy. The research results of this paper provide theoretical basis for realizing static and dynamic high precision mobile phone positioning algorithms. |
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| Keywords: | mobile APP positioning least squares method static Kalman filter dynamic Kalman filter |
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