RSS-Based Wireless LAN Indoor Localization and Tracking Using Deep Architectures

Muhammed Zahid Karakusak

doi:10.3390/bdcc6030084

RSS-Based Wireless LAN Indoor Localization and Tracking Using Deep Architectures

2022-08-08

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Muhammed Zahid Karakusak *

DOI：10.3390/bdcc6030084

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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.

Keywords:

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)

RSS-Based Wireless LAN Indoor Localization and Tracking Using Deep Architectures

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