In recent years, deep learning has been used for Wi-Fi fingerprint-based localization to achieve a remarkable performance, which is expected to satisfy the increasing requirements of indoor location-based service (LBS). In this paper, we propose a Wi-Fi fingerprint-based indoor mobile user localization method that integrates a stacked improved sparse autoencoder (SISAE) and a recurrent neural network (RNN). We improve the sparse autoencoder by adding an activity penalty term in its loss function to control the neuron outputs in the hidden layer. The encoders of three improved sparse autoencoders are stacked to obtain high-level feature representations of received signal strength (RSS) vectors, and an SISAE is constructed for localization by adding a logistic regression layer as the output layer to the stacked encoders. Meanwhile, using the previous location coordinates computed by the trained SISAE as extra inputs, an RNN is employed to compute more accurate current location coordinates for mobile users. The experimental results demonstrate that the mean error of the proposed SISAE-RNN for mobile user localization can be reduced to 1.60 m.

中文翻译：

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使用深度学习的基于Wi-Fi指纹的室内移动用户本地化

近年来，深度学习已用于基于Wi-Fi指纹的定位，以实现卓越的性能，有望满足室内基于位置的服务（LBS）不断增长的需求。在本文中，我们提出了一种基于Wi-Fi指纹的室内移动用户定位方法，该方法将堆叠的改进型稀疏自动编码器（SISAE）和递归神经网络（RNN）集成在一起。通过在损失函数中添加活动惩罚项以控制隐藏层中的神经元输出，我们改进了稀疏自动编码器。将三个改进的稀疏自动编码器的编码器进行堆叠以获得接收信号强度（RSS）向量的高级特征表示，并通过将逻辑回归层作为输出层添加到堆叠的编码器来构造SISAE进行定位。与此同时，使用由训练有素的SISAE计算的先前位置坐标作为额外输入，RNN用于为移动用户计算更准确的当前位置坐标。实验结果表明，所提出的针对移动用户定位的SISAE-RNN的平均误差可以降低到1.60 m。