An Accurate and Robust Approach of Device-Free Localization With Convolutional Autoencoder

Abstract

Device-free localization (DFL), as an emerging technology that locates targets without any attached devices via wireless sensor networks, has spawned extensive applications in the Internet of Things (IoT) field. For DFL, a key problem is how to extract significant features to characterize raw signals with different patterns associated with different locations. To address this problem, in this paper, the DFL problem is formulated as an image classification problem. Moreover, we design a three-layer convolutional autoencoder (CAE) neural network to perform unsupervised feature extraction from raw signals followed by supervised fine-tuning for classification. The CAE combines the advantages of a convolutional neural network (CNN) and a deep autoencoder (AE) in the feature learning and signals reconstruction, which is expected to achieve good performance for DFL. The experimental results show that the proposed approach can achieve a high localization accuracy rate of 100% for a reasonable grid size on the raw real-world data, i.e., the collected raw data without added Gaussian noise, and is robust to noisy data with a signal-to-noise ratio greater than −5 dB. Additionally, its time cost for the classification of a single activity is 4 ms, which is fast enough for the IoT applications. The proposed approach outperforms the deep CNN and AE in terms of localization accuracy and robust ability against noise.