Hidden Markov Model based mobility learning fo improving indoor tracking of mobile users

Troels Laursen, N. B. Pedersen, J. Nielsen, T. Madsen
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引用次数: 9

Abstract

Indoors, a user's movements are typically confined by walls, corridors, and doorways, and further he is typically repeating the same movements such as walking between certain points in the building. Conventional indoor localization systems do usually not take these properties of the user's movements into account. In this paper we propose a Hidden Markov Model (HMM) based tracking algorithm, which takes a user's previous movements into account. In a quantized grid representation of an indoor scenario, past movement information is used to update the HMM transition probabilities. The user's most likely trajectory is then calculated using and extended version of the Viterbi algorithm. The results show significant improvements of the proposed algorithm compared to a simpler moving average smoothing.
基于隐马尔可夫模型的移动学习改进室内移动用户跟踪
在室内,用户的动作通常受到墙壁、走廊和门道的限制,而且他通常会重复相同的动作,例如在建筑物的某些点之间行走。传统的室内定位系统通常不考虑用户运动的这些特性。本文提出了一种基于隐马尔可夫模型(HMM)的跟踪算法,该算法考虑了用户先前的运动。在室内场景的量化网格表示中,使用过去的运动信息来更新HMM转移概率。然后使用Viterbi算法的扩展版本计算用户最可能的轨迹。结果表明,与简单的移动平均平滑相比,该算法有显著的改进。
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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