Recognition of ultrasonic multi-echo sequences for autonomous symbolic indoor tracking

André Stuhlsatz
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引用次数: 11

Abstract

This paper presents an autonomous symbolic indoor tracking system for ubiquitous computing applications. The proposed approach is based upon the assumption that topologically discriminable information can be assigned explicitly to different spaces of a given indoor environment. On that assumption, continuous time-of-flight (ToF) measurements of echo-bursts obtained from four orthogonally and coplanarly mounted ultrasonic transducer are used to learn a stochastic room model. While the individual acoustic representation of space is captured using Gaussian mixture densities, the stochastic variabilities in the moving direction of a person are modeled by hidden-Markov-models (HMMs). Experiments within a six room environment resulted in a room recognition rate of 92.21% and a room sequence recognition rate of 66.00% without any pre-fixed devices.
用于自主符号室内跟踪的超声多回波序列识别
本文提出了一种适用于泛在计算应用的自主符号室内跟踪系统。所提出的方法基于拓扑可判别信息可以明确分配到给定室内环境的不同空间的假设。在此假设下,利用四个正交共面安装的超声换能器获得的连续飞行时间(ToF)测量值来学习随机房间模型。使用高斯混合密度捕获空间的单个声学表示,而使用隐马尔可夫模型(hmm)模拟人的移动方向的随机变量。在6个房间环境下进行实验,在没有任何预先固定设备的情况下,房间识别率为92.21%,房间序列识别率为66.00%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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