With the increasing popularization and development of WiFi devices, nowadays WiFi-based indoor localization has become a hot topic. Traditional Wi-Fi-based localization technologies which utilize received signal strength indication suffer from indoor multi-path effects and result in localization performance degradation. Therefore, choosing the appropriate characteristic of the WiFi signal is crucial for indoor localization. To improve the localization accuracy, we propose PLAP, a passive localization method using amplitude and phase of channel state information (CSI). Specifically, Hampel filter is used to process the amplitude signals and linear transformation is employed for calibrating phases. To extract representative features from calibrated amplitude and phase signals, we developed a deep learning framework which combines a convolutional neural network (CNN) and a bi-directional Gated recurrent unit (BGRU) to estimate the location of an objective. The experimental results show that the proposed PLAP outperforms other baselines with real-world evaluation.

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