基于误差预测的自适应UWB/PDR融合定位算法

doi:10.11772/j.issn.1001-9081.2019101830

计算机应用 ›› 2020, Vol. 40 ›› Issue (6): 1755-1762.DOI: 10.11772/j.issn.1001-9081.2019101830

基于误差预测的自适应UWB/PDR融合定位算法

张健铭^1,2, 施元昊^1,3, 徐正蓺^1,2, 魏建明¹

1.中国科学院上海高等研究院，上海 201210
2.中国科学院大学，北京 100049
3.上海大学通信与信息工程学院，上海 200444

收稿日期:2019-10-28 修回日期:2019-12-25 出版日期:2020-06-10 发布日期:2020-06-18
通讯作者: 徐正蓺(1987—)
作者简介:张健铭(1995—)，男，北京人，硕士研究生，主要研究方向：数据融合、机器学习.施元昊(1994—)，男，浙江绍兴人，硕士研究生，主要研究方向：惯性传感器应用、深度学习.徐正蓺(1987—)，男，上海人，博士，主要研究方向：无线传感网络、数据融合.魏建明(1973—)，男，湖南永州人，研究员，博士，主要研究方向：城市公共安全、先进监测与预警.
基金资助:
上海市科技创新行动计划项目（19DZ1202200）；上海市青年科技英才扬帆计划（18YF1425600）。

Adaptive UWB/PDR fusion positioning algorithm based on error prediction

ZHANG Jianming^1,2, SHI Yuanhao^1,3, XU Zhengyi^1,2, WEI Jianming¹

1. Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China

Received:2019-10-28 Revised:2019-12-25 Online:2020-06-10 Published:2020-06-18
Contact: XU Zhengyi, born in 1987, Ph. D. His research interests include wireless sensor network, data fusion.
About author:ZHANG Jianming, born in 1995, M. S. candidate. His research interests include data fusion, machine learning.SHI Yuanhao, born in 1994, M. S. candidate. His research interests include inertial sensor application, deep learning.XU Zhengyi, born in 1987, Ph. D. His research interests include wireless sensor network, data fusion.WEI Jianming, born in 1973, Ph. D., research fellow. His research interests include public safety of city, advanced monitoring and warning.
Supported by:
Shanghai Science and Technology Innovation Action Plan (19DZ1202200), the Shanghai Sailing Program (18YF1425600).

摘要/Abstract

摘要： 为了解决在室内非视距(NLOS)定位场景中超宽带(UWB)技术性能不佳、航位推算(PDR)算法累积误差过大的问题，以及由环境因素引起的UWB性能下降的问题，提出了一种基于UWB误差预测而自适应系数调节的UWB/PDR融合定位算法。该算法创新地提出了利用支持向量机(SVM)回归模型对复杂环境中UWB定位误差进行预测，并以此为基础，为常规的扩展卡尔曼滤波(EKF)算法添加了自适应调节系数，以提高UWB/PDR的融合定位效果。实验结果表明，所提算法在复杂UWB环境中可以有效预测当前UWB定位误差水平，并通过自适应调整融合系数提高精度，使得较常规EKF算法在一般区域的定位误差降低了18.2%，在UWB精度较差的区域中的定位误差降低了48.7%，从而减小了环境对UWB性能的影响；在包含UWB的视距内(LOS)及NLOS的复杂场景中，通过融合定位算法，将定位每百米误差由米级降低至分米级，解决了NLOS场景中PDR 误差过大的问题。

关键词: 室内定位, 支持向量机, 扩展卡尔曼滤波, 自适应, 融合定位

Abstract: An Ultra WideBand (UWB)/ Pedestrian Dead Reckoning (PDR) fusion positioning algorithm with adaptive coefficient adjustment based on UWB error prediction was proposed in order to improve the UWB performance and reduce the PDR accumulative errors in the indoor Non-Line-Of-Sight (NLOS) positioning scenes and solve the UWB performance degradation caused by environmental factors. On the basis of the creative proposal of predicting the UWB positioning errors in complex environment by Support Vector Machine (SVM) regression model, UWB/PDR fusion positioning performance was improved by adding adaptive adjusted parameters to the conventional Extended Kalman Filter (EKF) algorithm. The experimental results show that the proposed algorithm can effectively predict the current UWB positioning errors in the complex UWB environment, and increase the accuracy by adaptively adjusting the fusion parameters, which makes the positioning error reduced by 18.2% in general areas and reduced by 48.7% in the areas with poor UWB accuracy compared with those of the conventional EKF algorithm, so as to decrease the environmental impact on the UWB performance. In complex scenes of both Line-Of-Sight (LOS) and NLOS including UWB, the positioning error per 100 meters is reduced from meter scale to decimeter scale, which reduces the PDR errors in NLOS scenes.

Key words: indoor positioning, Support Vector Machine (SVM), Extended Kalman Filter (EKF), adaptive, fusion positioning

中图分类号:

TP212.9

张健铭, 施元昊, 徐正蓺, 魏建明. 基于误差预测的自适应UWB/PDR融合定位算法[J]. 计算机应用, 2020, 40(6): 1755-1762.

ZHANG Jianming, SHI Yuanhao, XU Zhengyi, WEI Jianming. Adaptive UWB/PDR fusion positioning algorithm based on error prediction[J]. Journal of Computer Applications, 2020, 40(6): 1755-1762.

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基于误差预测的自适应UWB/PDR融合定位算法

Adaptive UWB/PDR fusion positioning algorithm based on error prediction

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