基于互补滤波融合WiFi和PDR的行人室内定位

doi:10.13474/j.cnki.11-2246.2019.0140

测绘通报 ›› 2019, Vol. 0 ›› Issue (5): 12-15,34.doi: 10.13474/j.cnki.11-2246.2019.0140

基于互补滤波融合WiFi和PDR的行人室内定位

朱家松^1,2, 程凯^1,2, 周宝定^1,2, 林伟东¹

1. 深圳大学土木工程学院, 广东深圳 518060;
2. 深圳大学空间信息智能感知与服务深圳市重点实验室, 广东深圳 518060

收稿日期:2018-12-17 出版日期:2019-05-25 发布日期:2019-06-04
通讯作者: 程凯。E-mail:2452587090@qq.com E-mail:2452587090@qq.com
作者简介:朱家松(1975-),男,副教授,主要从事智能交通系统研究。E-mail:zjsong@szu.edu.cn
基金资助:
国家自然科学基金（41701519）；测绘遥感信息工程国家重点实验室资助项目（16I02）；广东省自然科学基金（2017A030310544）

Integration of WiFi and PDR based complementary filtering indoor localization

ZHU Jiasong^1,2, CHENG Kai^1,2, ZHOU Baoding^1,2, LIN Weidong¹

1. School of Civil Engineering, Shenzhen University, Shenzhen 518060, China;
2. Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China

Received:2018-12-17 Online:2019-05-25 Published:2019-06-04

摘要/Abstract

摘要： 提出了一种基于互补滤波融合WiFi和PDR的行人室内定位方法。首先改善WiFi位置指纹定位的KNN算法，通过阈值的设定，排除相似度高但实际上不可能的点，获取动态K值；然后通过行人航位推算（PDR）初始化算法，动态轨迹概率计算，确定PDR初始位置；最后在改进的WiFi和PDR的定位基础上，基于互补滤波原理，根据WiFi和PDR定位的不同特性，利用各自的定位优点，使用WiFi定位修正PDR的定位结果，通过相应权重参数的调整，输出最终融合定位结果。试验过程中，选取3种不同的室内环境区域，试验结果证明了该算法可大大提高室内定位的精度和稳定性。

关键词: 室内定位, 位置指纹, 行人航位推算, 互补滤波

Abstract: This paper presents a pedestrian indoor location method based on complementary filtering fusion WiFi and PDR. Firstly, we improve the KNN algorithm of WiFi position fingerprint location. By setting threshold, we can get the dynamic K value by eliminating the points with high similarity but practically impossible. Secondly, we determine the initial position of PDR by initializing the pedestrian dead reckoning (PDR) algorithm and calculating the dynamic trajectory probability. Finally, on the basis of the improved positioning of WiFi and PDR, based on the principle of complementary filtering, according to the different characteristics of WiFi and PDR positioning, using their respective positioning advantages, using WiFi positioning to modify the positioning results of PDR, through adjusting the corresponding weight parameters, the final fusion positioning results are output. During the experiment, we choose three different indoor environment areas. The experimental results show that the algorithm can greatly improve the accuracy and stability of indoor positioning.

Key words: indoor localization, location fingerprint, pedestrian dead reckoning, complementary filtering

中图分类号:

P228

朱家松, 程凯, 周宝定, 林伟东. 基于互补滤波融合WiFi和PDR的行人室内定位[J]. 测绘通报, 2019, 0(5): 12-15,34.

ZHU Jiasong, CHENG Kai, ZHOU Baoding, LIN Weidong. Integration of WiFi and PDR based complementary filtering indoor localization[J]. 测绘通报, 2019, 0(5): 12-15,34.

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基于互补滤波融合WiFi和PDR的行人室内定位

Integration of WiFi and PDR based complementary filtering indoor localization

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