WiFi RSS and RTT Indoor Positioning with Graph Temporal Convolution Network
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a hybrid Graph-Temporal Convolutional Network (GTCN) for accurate indoor positioning using WiFi signals. The GTCN model achieves high accuracy by combining Access Point geometry and temporal signal dynamics, even in challenging Non-Line-Of-Sight conditions.
Area Of Science
- Computer Science
- Electrical Engineering
- Robotics
Background
- Indoor positioning systems (IPS) are crucial for various applications.
- Achieving sub-meter accuracy with commodity WiFi is hindered by multipath fading and Non-Line-Of-Sight (NLOS) propagation.
- Existing methods struggle with diverse indoor layouts and dynamic environments.
Purpose Of The Study
- To develop a novel hybrid Graph-Temporal Convolutional Network (GTCN) for high-accuracy indoor positioning.
- To enhance robustness by jointly utilizing WiFi Received Signal Strength (RSS) and Round-Trip Time (RTT) features.
- To create a computationally efficient model suitable for real-time deployment on edge devices.
Main Methods
- Proposed a hybrid GTCN model integrating graph convolutions for Access Point (AP) geometry and dilated temporal convolutional networks for signal dynamics.
- Implemented a lightweight gating mechanism for adaptive per-AP importance learning.
- Evaluated the model using both WiFi RSS and RTT measurements across diverse indoor environments.
Main Results
- The GTCN model demonstrated high positioning accuracy across lecture theatres, offices, corridors, and building floors.
- Positioning accuracy improved with increased AP density, particularly in large-scale mixed environments under Line-Of-Sight (LOS) and NLOS conditions.
- The model requires fewer than 105 trainable parameters and tens of MFLOPs per inference, enabling real-time performance.
Conclusions
- The proposed GTCN model offers a robust and accurate solution for indoor positioning using commodity WiFi.
- The hybrid approach effectively addresses challenges posed by multipath fading and NLOS effects.
- The model's computational efficiency makes it suitable for real-time applications on embedded and edge computing platforms.
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