Adaptive Scheme of Denoising Autoencoder for Estimating Indoor Localization Based on RSSI Analytics in BLE Environment

Abstract

In indoor environments, estimating localization using a received signal strength indicator (RSSI) is difficult because of the noise from signals reflected and refracted by walls and obstacles. In this study, we used a denoising autoencoder (DAE) to remove noise in the RSSI of Bluetooth Low Energy (BLE) signals to improve localization performance. In addition, it is known that the signal of an RSSI can be exponentially aggravated when the noise is increased proportionally to the square of the distance increment. Based on the problem, to effectively remove the noise by adapting this characteristic, we proposed adaptive noise generation schemes to train the DAE model to reflect the characteristics in which the signal-to-noise ratio (SNR) considerably increases as the distance between the terminal and beacon increases. We compared the model’s performance with that of Gaussian noise and other localization algorithms. The results showed an accuracy of 72.6%, a 10.2% improvement over the model with Gaussian noise. Furthermore, our model outperformed the Kalman filter in terms of denoising.

Wi-Fi-Based Indoor Localization and Navigation: A Robot-Aided Hybrid Deep Learning Approach

“…They train multiple weak learners (decision trees) by using the boosting or bagging method on the training set, and they integrate these learners into a strong learner for the final prediction. In recent years, deep learning (DL) has been increasingly used in the Wi-Fi-based positioning and navigation algorithms [ 14 , 15 , 16 , 17 , 18 , 19 , 32 ], such as DeepFi [ 32 ] and WiDeep [ 14 ], which adopt the channel state information (CSI) or the RSSI data from all subcarriers to train a neural network with more layers than the MLP network. Autoencoder (AE) is a popular network structure for feature extraction and is widely applied into these algorithms by developing different variants such as denoising AE.…”

“…In recent years, many machine learning (ML) algorithms [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ] have been applied to the fingerprint-based positioning system. They could learn useful knowledge from multi-dimensional measured data with position labels to reduce the effect of RSSI fluctuation and improve fingerprinting accuracy and system robustness.…”