Abstract
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.
Highlights
As the development of the fifth-generation (5G) networks, the key technologies of 5G are paving the way for applications of Internet of Things (IoT) [1,2,3], for they can greatly improve the network connectivity [1], spectrum efficiency [3], and so on
The cumulative probabilities of the stacked autoencoder (SAE), sparse autoencoder (SSAE), and stacked improved sparse autoencoder (SISAE) with feature learning within a localization error of 1 m are obviously higher than that of the deep neural network (DNN), which proves the effectiveness of the received signal strength (RSS) feature learning
We propose a Wi-Fi fingerprint-based indoor mobile user localization method that integrates an SISAE and an recurrent neural network (RNN)
Summary
As the development of the fifth-generation (5G) networks, the key technologies of 5G are paving the way for applications of Internet of Things (IoT) [1,2,3], for they can greatly improve the network connectivity [1], spectrum efficiency [3], and so on. Indoor radio propagation is time-varying and affected by multipath effect, shadowing effect, and environmental dynamics, which could degrade the performance of Wi-Fi fingerprinting localization, let alone the localization for mobile users with only limited RSS data available. Because the dimensions of the hidden layers of the SSAE could be greater than the dimension of its input layer, it can be applied to learn RSS features in the scenarios with a few deployed APs. as a supervised deep learning algorithm, a recurrent neural network (RNN) is powerful for processing sequential correlation data and is able to improve the localization accuracy for mobile users. We propose a Wi-Fi fingerprintbased indoor mobile user localization method that integrates a stacked improved sparse autoencoder (SISAE) and an RNN to achieve high localization accuracy.
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