使用AP逆位置估计的CNN定位

S. Aikawa, Shinichiro Yamamoto, Takuma Muramatsu
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引用次数: 5

摘要

本文主要研究了基于无线局域网接入点(AP) RSSI的指纹定位方法。近年来,关于深度学习定位方法的讨论非常活跃。我们提出了基于卷积神经网络(CNN)的指纹识别方法。建立ap之间的邻接关系作为二维模型,并利用它来制作指纹定位的CNN模型。为了验证该方案对定位精度的提高,我们用实验数据进行了验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CNN Localization using AP Inverse Position Estimation
This contribution focuses on indoor localization by Finger Print method using RSSI of wireless LAN access point (AP). In recent years, there are lively animated discussions on the localization methods using Deep Learning. We proposed Finger Print based on the Convolutional Neural Network (CNN). Establish the adjacency relationship among APs as a two-dimensional model and use it to make the CNN model for Finger Print localization. In order to confirm the improvement of the localization accuracy by this proposal, we verified by experimental data.
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