A fingerprinting indoor localization algorithm based deep learning

G. Felix, Mario Siller, Ernesto Navarro-Alvarez
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引用次数: 81

Abstract

Fingerprinting in essence uses a machine to infer physicals locations from radio map data. This machines are usually either probabilistic and neural networks consisting of one layer. In this propose we use deeper machines (DNN, DBN and GB-DBN) to increase the estimation accuracy and reduce generalization error on dynamic indoor environment. Also we investigated the impact of pre-training algorithm on fingerprinting indoor location systems. Experimental results demonstrate that deep models provide an efficient generalization performance on indoor environments. They have the disadvantage that demand high processing resources when they are trained on off-line phase, however, deep models are swift to predict during on-line phase.
基于深度学习的指纹室内定位算法
指纹识别本质上是使用一台机器从无线电地图数据推断物理位置。这种机器通常要么是概率网络,要么是由一层组成的神经网络。本文采用深度神经网络(DNN)、DBN和GB-DBN来提高动态室内环境下的估计精度和减小泛化误差。研究了预训练算法对指纹室内定位系统的影响。实验结果表明,深度模型在室内环境下具有良好的泛化性能。深度模型在离线阶段训练时需要大量的处理资源,而在线阶段预测速度快。
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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