Without any preinstalled infrastructure, pedestrian dead reckoning (PDR) is a promising indoor positioning technology for pedestrians carrying portable devices to navigate. Step detection and step length estimation (SLE) are two essential components for the pedestrian navigation based on PDR. To solve the overcounting problem, this study proposes a peak-valley detection method, which can remove the abnormal values effectively. The current step length models mostly depend on individual parameters that need to be predetermined for different users. Based on fuzzy logic (FL), we establish a rule base that can adjust the coefficient in the Weinberg model adaptively for every detected step of various human shapes walking. Specifically, to determine the FL rule base, we collect user acceleration data from 10 volunteers walking under the combination of diverse step length and stride frequency, and each one walks 49 times at all. The experimental results demonstrate that our proposed method adapts to different kinds of persons walking at various step velocities. Peak-valley detection can achieve an average accuracy of 99.77% during 500 steps of free walking. Besides, the average errors of 5 testers are all less than 4 m per 100 m and the smallest one is 1.74 m per 100 m using our coefficient self-determined step length estimation model.

中文翻译：

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基于峰谷检测的系数自确定步长估计模型

没有任何预装的基础设施，行人航位推测法（PDR）对于携带便携式设备进行导航的行人来说是一种很有前途的室内定位技术。步距检测和步长估计（SLE）是基于PDR的行人导航的两个基本组成部分。为了解决计数过多的问题，本研究提出了一种峰谷检测方法，可以有效地消除异常值。当前的步长模型主要取决于需要为不同用户预先确定的各个参数。基于模糊逻辑（FL），我们建立了一个规则库，可以针对各种人体形状行走的每个检测到的步骤自适应地调整Weinberg模型中的系数。具体来说，要确定FL规则库，我们收集了10名志愿者的用户加速度数据，这些志愿者在不同的步长和步幅频率的组合下行走，每个人总共行走49次。实验结果表明，我们提出的方法适用于以各种步速行走的不同类型的人。在500步自由行走期间，峰谷检测可以实现99.77％的平均准确度。此外，使用我们的系数自定步长估算模型，5个测试仪的平均误差均小于100 m 4 m，最小误差为100 m 1.74 m。在500步自由行走中，占77％。此外，使用我们的系数自定步长估算模型，5个测试仪的平均误差均小于100 m 4 m，最小误差为100 m 1.74 m。在500步自由行走中，占77％。此外，使用我们的系数自定步长估算模型，5个测试仪的平均误差均小于100 m 4 m，最小误差为100 m 1.74 m。