Shuyu Cui’s research while affiliated with Kyungpook National University and other places

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 m×7.27 m room with a 1×1 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.

With the development of wireless communication technology research, location-based services (LBS) have emerged due to provide useful information to users based on the current user locations or a given location-indoor positioning technology has become a hot spot for research. Indoor positioning technology based on WIFI signals attentions owing to WIFI networks cover various indoor environments. Received signal strength (RSS) based indoor positioning technology has become an effective indoor positioning technology as a method that utilizes the wireless signal propagation model. However, due to the complexity and variability of the indoor environment, RSS values are highly vulnerable to noise and multipath interference, RSS-based WIFI fingerprinting can effectively avoid this problem. During the construction of RSS fingerprint maps, fingerprint data collection is time-consuming and laborious, how to use a small amount of fingerprint data to achieve high-precision positioning becomes a difficult point for fingerprint positioning technology. To solve this problem, a high-precision indoor localization method based on spectral normalization for generative adversarial networks (SNGAN) is proposed. The generative adversarial network with spectral normalization not only retains the original information to the greatest extent during the training process, but also makes the generated data closer to the distribution of real data and speeds up the training. Collecting a small amount of fingerprint data achieves higher localization accuracy.