Differential NLOS Error Detection in UWB-based Localization Systems using Logistic Regression

Zandian, Reza; Witkowski, Ulf

doi:10.1109/wpnc.2018.8555750

Cited by 8 publications

(9 citation statements)

References 14 publications

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“…The purpose of classification algorithm is to distinguish LOS from different types of NLOS so as to reduce the influence of NLOS. In this paper, six different machine learning algorithms are selected [3,[6][7][8][9][10] : RF, SVM, Adaboost, LR, KNN and XGBoost. These algorithms are selected mainly to ensure the reliability of results.…”

Section: Experimental Setup and Verificationmentioning

confidence: 99%

“…The main principle is that the energy of the first path of LOS signal is significantly greater than that of other delay paths, while the first path of NLOS signal is often not the maximum energy path [3,4] . Recently many researchers focus on using machine learning methods to train CIR data to automatically identifying different obstacles [5] , which includes logical regression (LR) [6] , Random Forest (RF) [7] , support vector machine (SVM) [8][9][10] , K-nearest neighbor method (KNN) [3] etc. However, most literatures only consider one type of NLOS and therefore to treat this problem as binary classification [6,7,[10][11][12][13] .…”

Section: Introductionmentioning

confidence: 99%

“…Recently many researchers focus on using machine learning methods to train CIR data to automatically identifying different obstacles [5] , which includes logical regression (LR) [6] , Random Forest (RF) [7] , support vector machine (SVM) [8][9][10] , K-nearest neighbor method (KNN) [3] etc. However, most literatures only consider one type of NLOS and therefore to treat this problem as binary classification [6,7,[10][11][12][13] . Even signals were collected in multiple NLOS scenario such as wall, wooden door, glass, etc., these NLOS data were only put together as a kind of NLOS for binary classification [14] .…”

Section: Introductionmentioning

confidence: 99%

“…In addition, when evaluating the accuracy of the machine learning algorithms, the test set and training set used in existing literatures are measured at the same position without testing the performance through data independent of the training set [6,7,[10][11][12][13][14][15] which is not practical because the scope of test set is always much larger than that of training set, and UWB mobile nodes may distribute in any corner of the room.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dataset collecting for UWB NLOS identification based on machine learning

Zhang,

Liu,

Hao

2023

Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023)

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The Non-Line-of-Sight (NLOS) environment significantly reduces the accuracy of Ultra-Wideband (UWB) ranging and positioning in the UWB positioning system based on time of arrival. The mainstream method is to identify the Line-of-Sight (LOS) and NLOS environment through machine learning and make corresponding corrections to improve the accuracy of the positioning system. However, the existing research and application of machine learning methods do not fully consider the situation that the test set exceeds the training set, which is inconsistent with the actual scenario. Firstly, in this paper, we show through experimental results that when the test set and the training set are independent, different training set acquisition methods will directly affect the accuracy of machine learning. The denser the training set, the higher the precision and the greater the workload. Therefore, aiming at how to choose the training set collection scheme, we put forward an evaluation index to determine the best training set collection scheme. Commonly used machine learning algorithms, such as Random Forest (RF), and XGBoost, are used here to explore the parameter configuration of this index. The research results show that when using machine learning method to identify ultra-wideband NLOS scenes, the acquisition step size of training set is 1.6 meters, which can make the identification accuracy high and the acquisition workload small.

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Section: Experimental Setup and Verificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dataset collecting for UWB NLOS identification based on machine learning

Zhang,

Liu,

Hao

2023

Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023)

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show abstract

“…A logistic regression-based localization approach is an alternative way to improve the UWB localization performance for the non-line-of-sight (NLOS) condition. The regression model presented in Reference [20] addressed the NLOS detection error mitigation of differential time of arrival (TDoA) topologies and suppresses the NLOS error up to 80%. In Reference [21], the authors also introduced another regression-based localization method for identifying the LOS conditions.…”

Section: Related Workmentioning

confidence: 99%

UWB Indoor Localization Using Deep Learning LSTM Networks

2020

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Localization using ultra-wide band (UWB) signals gives accurate position results for indoor localization. The penetrating characteristics of UWB pulses reduce the multipath effects and identify the user position with precise accuracy. In UWB-based localization, the localization accuracy depends on the distance estimation between anchor nodes (ANs) and the UWB tag based on the time of arrival (TOA) of UWB pulses. The TOA errors in the UWB system, reduce the distance estimation accuracy from ANs to the UWB tag and adds the localization error to the system. The position accuracy of a UWB system also depends on the line of sight (LOS) conditions between the UWB anchors and tag, and the computational complexity of localization algorithms used in the UWB system. To overcome these UWB system challenges for indoor localization, we propose a deep learning approach for UWB localization. The proposed deep learning model uses a long short-term memory (LSTM) network for predicting the user position. The proposed LSTM model receives the distance values from TOA-distance model of the UWB system and predicts the current user position. The performance of the proposed LSTM model-based UWB localization system is analyzed in terms of learning rate, optimizer, loss function, batch size, number of hidden nodes, timesteps, and we also compared the mean localization accuracy of the system with different deep learning models and conventional UWB localization approaches. The simulation results show that the proposed UWB localization approach achieved a 7 cm mean localization error as compared to conventional UWB localization approaches.

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LOS/NLOS Detection in UWB Localization System with Anchors Deployment Along a Narrow Path

He,

Huang,

2024

Lecture Notes in Electrical Engineering

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Differential NLOS Error Detection in UWB-based Localization Systems using Logistic Regression

Cited by 8 publications

References 14 publications

Dataset collecting for UWB NLOS identification based on machine learning

Dataset collecting for UWB NLOS identification based on machine learning

UWB Indoor Localization Using Deep Learning LSTM Networks

LOS/NLOS Detection in UWB Localization System with Anchors Deployment Along a Narrow Path

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