Multi-task space-time fusion Wi-Fi fingerprint positioning system

By combining a denoising autoencoder, a generative adversarial network, and a channel attention mechanism, a multi-task learning WiFi fingerprint indoor positioning model is constructed, which solves the problems of signal fluctuation and sample imbalance, and achieves high-precision and robust indoor positioning.

CN120881733APending Publication Date: 2025-10-31CHANGCHUN UNIV OF TECH
0 Cites 1 Cited by

Patent Information

Application Number
CN202511241692.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-09-02
Publication Date
2025-10-31

AI Technical Summary

Technical Problem

Existing WiFi fingerprinting positioning methods face problems such as signal fluctuations, sample imbalance, and lack of spatiotemporal modeling capabilities in complex indoor environments, resulting in insufficient positioning accuracy and robustness.

Method used

By employing a denoising autoencoder (DAE), generative adversarial network (GAN), channel attention mechanism (ECA), and multi-task learning strategy, combined with CNN-GRU, an end-to-end localization model is constructed to achieve joint prediction of building, floor, and spatial coordinates, thereby improving data quality and multi-task collaborative optimization.

Benefits of technology

It significantly improves the accuracy and robustness of indoor positioning, adapts to changes in behavior patterns in complex environments, and enhances the model's generalization ability and positioning performance.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention provides a multi-task space-time fusion Wi-Fi fingerprint positioning system aiming at the problems that Wi-Fi signals are easily interfered in a complex indoor environment and the generalization ability of a traditional method is weak. The system takes spatio-temporal feature fusion as a core, deep features are extracted from space and time dimensions, building classification, floor classification and coordinate regression targets are optimized through weight collaboration, and the generalization and discrimination performance of multi-scale positioning is remarkably improved. In order to further enhance the robustness of the model, channel attention, a generative adversarial network and a de-noising auto-encoder are introduced to enhance feature expression and data diversity. Experiments on a UJIIndoorLoc data set show that the method is superior to some traditional methods and existing neural network models in the aspects of building and floor recognition accuracy and average positioning error, and the effectiveness of the method in a complex indoor environment is verified.
Need to check novelty before this filing date? Find Prior Art