基于人工神经网络的步长计数器的研制

IF 1.2 Q4 TELECOMMUNICATIONS
J. Kupke, T. Willemsen, Friedrich Keller, H. Sternberg
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引用次数: 13

摘要

室内导航的研究领域主要集中在gnss阴影区域的位置估计。加速度计和陀螺仪作为微机电系统用于基于行人航位推算的位置估计,其中加速度计用于步长检测。为了实现对行人导航有用的位置估计,步长的距离精度必须小于几厘米才能达到这个精度,因为距离误差加起来与时间有关。这个步长是由智能手机的集成加速度计的传感器数据得出的。本文介绍了一种基于人工神经网络的步长估计步长计数器。利用Matlab神经网络工具箱生成人工神经网络的结构。将三轴加速度计调平后,利用z轴加速度来实现基于四十多人数据的人工神经网络。此外,将结果与另一种方法进行比较,同时使用两个条件,这两个条件必须依次满足。研究结果表明,步长识别率为99.5%,平均距离误差为各自距离的9%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of a step counter based on artificial neural networks
The research field of indoor navigation focuses on the position estimate in GNSS-shaded (Global Navigation Satellite System) areas. Accelerometer and gyroscope as Micro Electro Mechanical Systems are used for a position estimate based on Pedestrian Dead Reckoning, where the accelerometer is used for step detection. To realize a usefull position estimate for pedestrian navigation, the distance accuracy of the step length has to be less than a few centimeters to reach this accuracy, because the distance errors add up themselves time-dependent. This step length is derived from the sensor data of the integrated accelerometer of the smartphone. In this article, a step counter with step length estimate based on a artificial neural network (ANN) is described. The Matlab toolbox Neural Network is used to generate the structure of ANN. After leveling the three axis accelerometer the z-axis acceleration will be used to realize a ANN based on data from more than forty persons. Besides that, the results will be compared to an alternative approach, while two conditions are used which successively must be fulfilled. The results of this investigation reveal a step recognition rate of 99.5% as well as an average distance error of 9% of the respective distance.
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来源期刊
CiteScore
3.70
自引率
8.70%
发文量
12
期刊介绍: The aim of this interdisciplinary and international journal is to provide a forum for the exchange of original ideas, techniques, designs and experiences in the rapidly growing field of location based services on networked mobile devices. It is intended to interest those who design, implement and deliver location based services in a wide range of contexts. Published research will span the field from location based computing and next-generation interfaces through telecom location architectures to business models and the social implications of this technology. The diversity of content echoes the extended nature of the chain of players required to make location based services a reality.
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