基于传感器丰富的智能手机的图像声源脉冲的室内行人自定位。

Indoor Pedestrian Self-Positioning Based on Image Acoustic Source Impulse Using a Sensor-Rich Smartphone.

机构信息

Ministry of Education Key Laboratory of Cognitive Radio and Information Processing, GuiLin University of Electronic Technology, Guilin 541004, China.

College of Information Science and Engineering, Guilin University of Technology, Guilin 541004, China.

出版信息

Sensors (Basel). 2018 Nov 26;18(12):4143. doi: 10.3390/s18124143.

DOI:10.3390/s18124143

PMID:30486301

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6308701/

Abstract

The ubiquity of sensor-rich smartphones provides opportunities for a low-cost method to track indoor pedestrians. In this situation, pedestrian dead reckoning (PDR) is a widely used technology; however, its cumulative error seriously affects its accuracy. This paper presents a method of combining infrastructure-free indoor acoustic self-positioning with PDR self-positioning, which verifies the rationality of PDR results through the acoustic constraint between a sound source and its image sources. We further determine the first-order echo delay measurements, thus obtaining the mobile user position. We verify that the proposed method can achieve a continuous self-positioning median error of 0.19 m, and the error probability below 0.12 m is 54.46%, which indicates its ability to eliminate PDR error, as well as its adaptability to environmental disturbances.

摘要

智能手机的普及为低成本跟踪室内行人提供了机会。在这种情况下，行人航位推算（PDR）是一种广泛使用的技术，但其累积误差严重影响了其精度。本文提出了一种将无基础设施的室内声自定位与 PDR 自定位相结合的方法，通过声源与其镜像声源之间的声学约束来验证 PDR 结果的合理性。我们进一步确定了一阶回波延迟测量值，从而获得移动用户的位置。验证结果表明，所提出的方法可以实现连续自定位中值误差为 0.19m，误差概率低于 0.12m 的概率为 54.46%，这表明该方法能够消除 PDR 误差，并且适应环境干扰。