基于双向LSTM网络的室内定位

Dong Pang, Xinyi Le
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引用次数: 0

摘要

室内定位是基于位置的室内环境服务蓬勃发展的产物。考虑到接入点(AP)的可用性和行业普及的低成本,基于WiFi指纹的定位工具是一种很有前途的工具。然而,由于多径效应的干扰,接收到的信号强度数据(RSS)很可能会出现波动,从而导致在定位结果中的传播误差。为了解决这个问题,我们提出了基于精细指纹的双向长短期记忆(bi-LSTM)神经网络,从测试的粗糙RSS数据中学习关键特征,获得提取的训练权值作为精细指纹(RFs)。提取的精细指纹特征对波动信号具有较强的鲁棒性,能够反映指纹的环境特性。在复杂的室内环境中,我们的bi-LSTM网络的有效性得到了证实,与我们之前的算法和其他基于rss的方法相比,准确率有了显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Indoor Localization Using Bidirectional LSTM Networks
Indoor localization witnessed the flourishing development in location based service for indoor environments. Regarding the availability of access points (AP) and its low cost for industry popularization, one of promising tool for localization is based on WiFi fingerprints. However, because of the interference of multi-path effects, the received signal strength data (RSS) are quite possibly to have fluctuated, thus they may result in propagation errors into localization results. In order to tackle this issue, We propose refined fingerprints based bidirectional long-short-term memory (bi-LSTM) neural network to learn the key features from the tested coarse RSS data, obtaining extracted trained weights as refined fingerprints(RFs). The extracted features of refined fingerprints are capable to demonstrate strong robustness with fluctuated signals and represent the environmental properties. The effectiveness of our bi-LSTM network is substantiated in the complex indoor environment, and accuracy is remarkably improved compared with our previous algorithm and other RSS-based approaches.
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