Device-Free Localization via Sparse Coding with Log-Regularizer

As an emerging technology, device-free localization (DFL), using wireless sensor network to detect targets who do not need carry any attached devices, has spawned extensive applications, e.g., intrusion detection or tracking in security safeguards. Many previous studies formulate DFL as a classification problem, but there are still several challenges in terms of accuracy, robustness, etc. In this paper, we exploit a new log-regularizer in the objective function for classification. With taking the distinctive ability of log-regularizer to measure sparsity, the proposed approach can achieve an accurate localization process with robust performance in the challenging environments. Even if the input data is severely polluted by noise with a level of SNR = −10 dB, our algorithm can still keep a high accuracy of 99.4%, which outperforms five other machine learning algorithms, e.g., deep auto-encoder, convolutional neural network, etc.