TR-ReFloc: A TR-based Framework for Recovering Missed RSS for WiFi indoor positioning in the offline and online phase

The research on WiFi-based fingerprint positioning has become an important topic in the field of indoor positioning due to its low-cost deployment and widespread applications. However, received signal strength (RSS) from access points (APs) may be missed due to factors such as Green WiFi or AP malfunctions. Traditional approach often set the missed RSS to a predetermined default value, which may not be accurate. We propose a fingerprint recovery framework based on tensor-ring nuclear norm minimization (TRNNM) algorithm which uses the correlation of RSS to recover missed fingerprints in both offline and online phases, called TR-ReFloc. In the offline phase, the radio map in tensor form is recovered by tensor ring (TR) decomposition. In the online phase, depending on different user requirements, there are two stages to generate the positioning results. First, rapid results are generated in coarse positioning stage. Next, high-precision results generated in fine positioning stage by recovering the missed fingerprints using the correlation of test points (TPs) are closed to the current TP. Experiments demonstrate that the proposed framework is robust for recovering missed RSS and can maintain localization accuracy within 2 m even in cases of high missing rate, demonstrating significant improvement over traditional methods. (c) 2023 Elsevier B.V. All rights reserved.

Automated summary

The research on WiFi-based fingerprint positioning is an important topic in indoor positioning due to its low-cost deployment and wide applications. However, the missing of received signal strength (RSS) from access points (APs) can occur due to factors like Green WiFi or AP malfunctions. A fingerprint recovery framework called TR-ReFloc is proposed, which utilizes the correlation of RSS to recover missed fingerprints in both offline and online phases. Experimental results demonstrate that the proposed framework is robust in recovering missed RSS and can maintain localization accuracy within 2 m even with a high missing rate, showing significant improvement over traditional methods.