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RAD-GAN: Radio Map Anomaly Detection for Fingerprint Indoor Positioning with GAN

Fingerprinting approach based on a radio map is one of the well-received methods for indoor localization. However, owing to the radio devices reconfigured or deteriorated, the radio map will be degraded, rendering the positioning outcomes unreliable. We introduce an unsupervised anomaly detection model, termed Radio Map Anomaly Detection for Fingerprint Indoor Positioning with GAN (RAD-GAN), which trains only the normal RSSI (Received Signal Strength Indication) samples to learn the normality distribution of the radio signal in the spatial indoor domain. We exploit an encoder-decoder-encoder neural network for latent feature extraction. Afterward, an adversarial training scheme is employed to minimize the reconstruction error both within RSSI space and latent feature space. The higher reconstruction deviation of the RSSI vector is indicative of an anomaly from a normal distribution. We evaluate RAD-GAN in a BLE (Bluetooth Low Energy) signal environment with the power gain actively modified and the public UJI long-term Wi-Fi fingerprinting dataset. Experimental results show that RAD-GAN is more sensitive and robust than the former state-of-the-art models.