Indoor Dual-indicator Precision Localization Network based on Multitask Learning

Abstract

With the continuous combination of the localization field and AI methods, the accuracy of localization services has been improving. For example, in the field of indoor Localization based on WiFi fingerprint signals can be used for indoor Localization, monitoring and tracking tasks, but still faces many unsolved problems, such as poor Localization accuracy, vague floor Localization, high consumption of algorithm training samples, and data security risks. In this paper, Dual-indicator Localization Network designed based on Multitask Learning is considered for indoor Dual-indicator real-time localization based on WiFi fingerprint signals. Simulation experiments are also designed, and the analysis of the results from several dimensions such as confusion matrix, t-SNE graph, and model scoring criterion shows that the proposed DLnet network is much better than the traditional Machine Learning methods with a balance of localization accuracy and localization complexity.