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Access-Point Centered Window-Based Radio-Map Generation Network.

Won-Yeol Kim¹, Soo-Ho Tae¹, Dong-Hoan Seo²

¹Department of Electrical and Electronics Engineering, Interdisciplinary Major of Maritime AI Convergence, Korea Maritime and Ocean University, Busan 49112, Korea.

Sensors (Basel, Switzerland)

|September 28, 2021

Summary

This study introduces an access-point (AP)-centered window (APCW) radio-map generation network (RGN) to improve indoor positioning. The APCW-RGN enhances radio-map prediction accuracy and reduces training time for real-time object tracking.

Keywords:

access-point-centered window adversarial learning fingerprinting radio-map generation network

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Area of Science:

Robotics
Computer Science
Electrical Engineering

Background:

Indoor radio-mapping positioning technologies, like fingerprinting, are crucial for real-time object tracking.
Generating accurate radio-maps requires extensive measurement processes and workflows, increasing costs and complexity.
Existing methods face challenges in robustness to access-point (AP) location changes and efficient utilization of similar indoor structures.

Purpose of the Study:

To propose a novel radio-map generation network (RGN) centered around access-point (AP) coordinates using an access-point (AP)-centered window (APCW) approach.
To minimize costs and enhance the usability and prediction accuracy of radio-maps for indoor positioning.
To develop a robust and efficient method for generating complete radio-maps from partitioned indoor space learning.

Main Methods:

Development of an access-point (AP)-centered window (APCW) radio-map generation network (RGN).
Utilizing adversarial learning methods with APCW as input for partitioned indoor space learning.
Combining radio-maps from individual APs to generate a comprehensive map, ensuring robustness to AP location changes.

Main Results:

The proposed APCW-based RGN achieved the highest accuracy among four tested learning models.
The model demonstrated enhanced radio-map prediction accuracy by extracting radio-map parts based on AP floor plan coordinates.
A root-mean-square error (RMSE) of 4.01 dBm was achieved, indicating high precision in radio-map generation.

Conclusions:

The APCW-based RGN is a highly accurate and efficient method for generating indoor radio-maps.
This approach significantly reduces training time and improves prediction accuracy compared to traditional methods.
The proposed technique offers robustness and enhanced utilization of similar structures for indoor positioning systems.