Indoor Positioning using DNN and RF Method Fingerprinting-based on Calibrated Wi-Fi RTT
Lila Rana, Jiabin Dong, Shu‐Yu Cui, Jinlong Li, Jungyu Hwang, Joon Goo Park
Abstract
Indoor Localization Systems (ILS) based on Wi-Fi which utilize Wi-Fi routers installed within indoor spaces, are particularly popular because of their low cost and availability. However, noise signals and multi-path problems can affect traditional Wi-Fi-based approaches, leading to high localization errors. To address these challenges of this approach, the 802.11mc protocol introduced a Fine Timing Measurement (FTM) frame, which adopts the two-way ranging technique. Using calibrated Round Trip Time (RTT) to remove the range offset in RTT at the initiator end and deep learning for indoor location recognition has become increasingly important in this context. In our work, we propose an indoor location method that combines calibrated Wi-Fi RTT fingerprinting with a range-based technique that utilizes a Deep Neural Network (DNN) regression and the Random Forest (RF) regression algorithm model. This approach leverages calibrated RTT to provide accurate distance measurements in indoor environments, resulting in more accurate distance measurements than the existing methods, such as Received Signal Strength (RSS) approach and Wi-Fi FTM based on DNN. The experiment area has 3 Access Points (APs) in a $9.55 \mathrm{m}\times 7.27\mathrm{m}$ room with a $1 \times 1\mathrm{m}$ grid covering 36 Reference Points (RPs). We trained the proposed model and predicted the location of a new fingerprint in the test dataset, achieving a Mean Squared Error (MSE) of 0.12 m and 0.11 m in reference and non-reference points, respectively. This shows how effectively the proposed model works for obtaining less localization error indoor positioning using calibrated Wi-Fi RTT fingerprinting.