Unsupervised anomaly detection of indoor location signals based on self-attention

Indoor fingerprint location technology based on radio signal is widely used in the field of indoor location because of high accuracy and low deployment cost. The change of indoor signal environment will directly affect the positioning accuracy. The deep neural network is also used in anomaly detection of time series data. On this basis, an unsupervised indoor location signal anomaly detection model based on self-attention mechanism was proposed. The input of model is normal fingerprint data that can be easily obtained without location tags. The attention module of the model focuses on extracting the correlation between different signal sources in the fingerprint data. It amplifies the distinguishability between normal and abnormal by combining the association errors and reconstruction errors, thus improving the accuracy of indoor location signal detection. The performance of the proposed model was evaluated in a bluetooth signal dataset collected in the laboratory and a public Wi-Fi dataset named UJIIndoorLoc