RSS-Based Wireless LAN Indoor Localization and Tracking Using Deep Architectures
Küçük Resim Yok
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Mdpi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Wireless Local Area Network (WLAN) positioning is a challenging task indoors due to environmental constraints and the unpredictable behavior of signal propagation, even at a fixed location. The aim of this work is to develop deep learning-based approaches for indoor localization and tracking by utilizing Received Signal Strength (RSS). The study proposes Multi-Layer Perceptron (MLP), One and Two Dimensional Convolutional Neural Networks (1D CNN and 2D CNN), and Long Short Term Memory (LSTM) deep networks architectures for WLAN indoor positioning based on the data obtained by actual RSS measurements from an existing WLAN infrastructure in a mobile user scenario. The results, using different types of deep architectures including MLP, CNNs, and LSTMs with existing WLAN algorithms, are presented. The Root Mean Square Error (RMSE) is used as the assessment criterion. The proposed LSTM Model 2 achieved a dynamic positioning RMSE error of 1.73 m, which outperforms probabilistic WLAN algorithms such as Memoryless Positioning (RMSE: 10.35 m) and Nonparametric Information (NI) filter with variable acceleration (RMSE: 5.2 m) under the same experiment environment.
Açıklama
Anahtar Kelimeler
Wireless LAN indoor positioning, position tracking, fingerprinting-based localization, Kernel Density Estimator (KDE), Received Signal Strength (RSS), Continuous Wavelet Transform (CWT), deep learning, Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM), Hyperparameter Optimization (HPO)
Kaynak
Big Data and Cognitive Computing
WoS Q Değeri
N/A
Scopus Q Değeri
Q2
Cilt
6
Sayı
3
