一种室内无线定位方法、装置、电子设备及存储介质
By collecting indoor wireless signal and environmental feature data, and using supervised learning algorithms and multi-task learning models for hierarchical learning, the problem of unbalanced task focus in indoor wireless positioning is solved, achieving intelligent and accurate indoor positioning and improving positioning accuracy and robustness.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA UNITED NETWORK COMM GRP CO LTD
- Filing Date
- 2024-09-12
- Publication Date
- 2026-07-17
AI Technical Summary
In existing indoor wireless positioning technologies, multi-task learning models pay uneven attention to different indoor wireless positioning tasks, resulting in large complexity assessment errors and thus affecting positioning accuracy.
By collecting indoor wireless signal and environmental feature data, supervised learning algorithms are used to evaluate task complexity, and a multi-task learning model is constructed for hierarchical learning, including shared feature extraction and multi-layer neural networks, to optimize the execution order of each indoor wireless positioning task.
It achieves intelligent and accurate indoor wireless positioning, improves the optimization learning ability and positioning accuracy of the multi-task model, reduces the dependence on signal strength data, and improves robustness and positioning efficiency.
Smart Images
Figure CN119255365B_ABST