Dynamic WiFi indoor positioning based on the multi-scale metric learning

Abstract

At present, most of the fingerprint-based positioning methods require the target to stay static in the process of data collection, which are not applicable to the target positioning in a moving state. Common dynamic localization and tracking algorithms such as Kalman Filter (KF) and Particle Filter (PF) need to estimate the position at the current time according to the estimated position at the previous time. If there is a positioning error at a certain time, the following positioning performance will be affected by the cumulative errors. To solve these problems, this paper proposes a dynamic indoor positioning method based on the multi-scale metric learning of the channel state information (CSI), named as CSI-MML. This method can realize dynamic positioning using CSI signals without carrying extra equipment. The proposed model of CSI-MML is trained and tested by constructing few-shot learning tasks, which is mainly composed of two parts: feature extraction and similarity metric. In order to fully extract the effective features of the samples, the attention mechanism is added to the feature extraction module of the network to enhance the model’s ability of extracting significant features. The similarity metric module measures the global similarity and local similarity between samples and fuses the two similarities by a similarity fusion layer. Through the multi-scale metric, we can improve the metric ability of CSI-MML, so as to improve the positioning accuracy of dynamic positioning. The experimental results show that compared with the commonly used dynamic location and tracking algorithms such as KF and PF, the positioning error of the proposed method will not accumulate and the average positioning error is smaller.