scholarly journals Deep Learning-Based Indoor Localization Using Received Signal Strength and Channel State Information

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 33256-33267 ◽  
Author(s):  
Chaur-Heh Hsieh ◽  
Jen-Yang Chen ◽  
Bo-Hong Nien
Keyword(s):  
Deep Learning ◽  
Channel State Information ◽  
Indoor Localization ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Channel State ◽  
State Information

scholarly journals Fusion of Channel State Information and Received Signal Strength for Indoor Localization Using a Single Access Point

2020 ◽  
Vol 12 (12) ◽  
pp. 1995
Author(s):  
David Sánchez-Rodríguez ◽  
Miguel A. Quintana-Suárez ◽  
Itziar Alonso-González ◽  
Carlos Ley-Bosch ◽  
Javier J. Sánchez-Medina
Keyword(s):  
Channel State Information ◽  
Indoor Localization ◽  
Signal Strength ◽  
Access Point ◽  
Received Signal Strength ◽  
Location Estimation ◽  
Channel State ◽  
State Information ◽  
Single Access ◽  
5 Ghz

In recent years, indoor localization systems based on fingerprinting have had significant advances yielding high accuracies. Those approaches often use information about channel communication, such as channel state information (CSI) and received signal strength (RSS). Nevertheless, these features have always been employed separately. Although CSI provides more fine-grained physical layer information than RSS, in this manuscript, a methodology for indoor localization fusing both features from a single access point is proposed to provide a better accuracy. In addition, CSI amplitude information is processed to remove high variability information that can negatively influence location estimation. The methodology was implemented and validated in two scenarios using a single access point located in two different positions and configured in 2.4 and 5 GHz frequency bands. The experiments show that the methodology yields an average error distance of about 0.1 m using the 5 GHz band and a single access point.

scholarly journals A Novel Indoor Ranging Algorithm Based on a Received Signal Strength Indicator and Channel State Information Using an Extended Kalman Filter

2020 ◽  
Vol 10 (11) ◽  
pp. 3687 ◽  
Author(s):  
Jingjing Wang ◽  
Joon Goo Park
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Channel State Information ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Cumulative Distribution ◽  
Estimation Accuracy ◽  
Channel State ◽  
Received Signal Strength Indicator ◽  
State Information

With the increasing demand of location-based services, the indoor ranging method based on Wi-Fi has become an important technique due to its high accuracy and low hardware requirements. The complicated indoor environment makes it difficult for wireless indoor ranging systems to obtain accurate distance measurements. This paper presents an Extended Kalman filter-based approach for indoor ranging by utilizing transmission channel quality metrics, including Received Signal Strength Indicator (RSSI) and Channel State Information (CSI). The proposed ranging algorithm scheme is implemented and validated with experiments in two typical indoor environments. A real indoor experiment demonstrates that the ranging estimation accuracy of our algorithms can be significantly enhanced compared with the typical algorithms. The ranging estimation accuracy is defined as the cumulative distribution function of the distance error.

scholarly journals NLOS Identification in WLANs Using Deep LSTM with CNN Features

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4057 ◽  
Author(s):  
Viet-Hung Nguyen ◽  
Minh-Tuan Nguyen ◽  
Jeongsik Choi ◽  
Yong-Hwa Kim
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Channel State Information ◽  
Recurrent Neural Network ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Line Of Sight ◽  
Channel State ◽  
State Information ◽  
Channel Conditions

Identifying channel states as line-of-sight or non-line-of-sight helps to optimize location-based services in wireless communications. The received signal strength identification and channel state information are used to estimate channel conditions for orthogonal frequency division multiplexing systems in indoor wireless local area networks. This paper proposes a joint convolutional neural network and recurrent neural network architecture to classify channel conditions. Convolutional neural networks extract the feature from frequency-domain characteristics of channel state information data and recurrent neural networks extract the feature from time-varying characteristics of received signal strength identification and channel state information between packet transmissions. The performance of the proposed methods is verified under indoor propagation environments. Experimental results show that the proposed method has a 2% improvement in classification performance over the conventional recurrent neural network model.

scholarly journals An Indoor Localization System Using Residual Learning with Channel State Information

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 574
Author(s):  
Chendong Xu ◽  
Weigang Wang ◽  
Yunwei Zhang ◽  
Jie Qin ◽  
Shujuan Yu ◽  
...  
Keyword(s):  
Channel State Information ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Location Based Services ◽  
Channel State ◽  
State Information ◽  
Localization Accuracy ◽  
Localization System ◽  
Increasing Demand ◽  
Degradation Problem

With the increasing demand of location-based services, neural network (NN)-based intelligent indoor localization has attracted great interest due to its high localization accuracy. However, deep NNs are usually affected by degradation and gradient vanishing. To fill this gap, we propose a novel indoor localization system, including denoising NN and residual network (ResNet), to predict the location of moving object by the channel state information (CSI). In the ResNet, to prevent overfitting, we replace all the residual blocks by the stochastic residual blocks. Specially, we explore the long-range stochastic shortcut connection (LRSSC) to solve the degradation problem and gradient vanishing. To obtain a large receptive field without losing information, we leverage the dilated convolution at the rear of the ResNet. Experimental results are presented to confirm that our system outperforms state-of-the-art methods in a representative indoor environment.

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