Non-Line-of-Sight Identification Based on Unsupervised Machine Learning in Ultra Wideband Systems

Fan, Jiancun; Awan, Ahsan Saleem

doi:10.1109/access.2019.2903236

Cited by 54 publications

(28 citation statements)

References 24 publications

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“…The proposed method is based on the Convolution Neural Network (CNN) Although supervised ML is widely used in the literature to identify NLOS signals, it is not quite feasible to use in the scenario where the environment often changes due to the movement of the furniture from one location to another location. To overcome this limitation, Fan et al [20] proposed an unsupervised approach called Expectation Maximization for Gaussian Mixture Models (EM-GMM) that discriminates the LOS and NLOS components. Specially they applied EM over GMM to find the maximum likelihood of a received signal to determine whether it belongs to LOS or NLOS distribution.…”

Section: A ML For Nlos Error Minimizationmentioning

confidence: 99%

“…Second, according to [14], DL can be used to directly extract features for NLOS/LOS classification in a dynamic network environment with time-varying channel impulse response (CIR). Third, based on [20] unsupervised ML approaches are useful for classification NLOS and LOS signal classification in an unknown environment where there is no labelled data.…”

Section: A ML For Nlos Error Minimizationmentioning

confidence: 99%

“…For example, the authors in [17]- [20] have applied supervised and unsupervised ML techniques for NLOS identification and mitigation while deep learning (DL) technique is applied for NLOS mitigation in [14]. A DL Recurrent neural network (RNN) has been used to cope with RSSI signal fluctuation by exploring its time domain correlation in [13].…”

Section: Introductionmentioning

confidence: 99%

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A Survey of Machine Learning for Indoor Positioning

et al. 2020

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Widespread proliferation of wireless coverage has enabled culmination of number of advanced location-based services (LBS). Continuous tracking of accurate physical location is the foundation of these services, which is a challenging task especially indoors. Multitude of techniques and algorithms have been proposed for indoor positioning systems (IPS's). However, accuracy, reliability, scalability and, adaptability to the environment still remain as challenges for widespread deployment. Especially, unpredictable radio propagation characteristics in vastly varying indoor environments plus access technology limitations contribute to these challenges. Machine learning (ML) approaches have been widely attempted recently to overcome these challenges with reasonable success. In this paper, we aim to provide a comprehensive survey of ML enabled localization techniques using most common wireless technologies. First, we provide a brief background on indoor localization techniques. Afterwards, we discuss various ML techniques (supervised and unsupervised) that could alleviate different challenges in indoor localization including Non-line-ofsight (NLOS) issue, device heterogeneity and environmental variations with reasonable complexity. The trade-offs among multitude of issues are discussed using numerous published results. We also discuss how the ML algorithms can be effectively used for fusing different technologies and algorithms to achieve a comprehensive IPS. In essence, this survey will serve as a reference material to acquire a detailed knowledge on recent development of machine learning for accurate indoor positioning. INDEX TERMS Indoor positioning system (IPS), location-based services (LBS), machine learning (ML), non-line-of-sight (NLOS), wireless positioning, indoor tracking.

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Section: A ML For Nlos Error Minimizationmentioning

confidence: 99%

Section: A ML For Nlos Error Minimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Survey of Machine Learning for Indoor Positioning

et al. 2020

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“…Consequently, several investigations of the NLOS identification process in UWB were examined in the literature using different ML techniques as a classifier such as SVM in [9,10,17,21], MLP in [23,37,38], boosted decision tree (BDT) in [38], recursive decision tree in [35], and other ML techniques such as kernel principal component analysis in [19], etc. Moreover, the unsupervised machine learning technique called "expectation maximization for Gaussian mixture models" was recently applied in [39] to classify the LOS and NLOS conditions in UWB measurement. Likewise, deep learning approaches such as the convolutional neural network (CNN) were also explored to distinguish the NLOS condition from LOS in UWB ranging [13,22].…”

Section: Identification Of the Nlos And Mp Conditions In The Literatumentioning

confidence: 99%

Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods

et al. 2020

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In ultra-wideband (UWB)-based wireless ranging or distance measurement, differentiation between line-of-sight (LOS), non-line-of-sight (NLOS), and multi-path (MP) conditions is important for precise indoor localization. This is because the accuracy of the reported measured distance in UWB ranging systems is directly affected by the measurement conditions (LOS, NLOS, or MP). However, the major contributions in the literature only address the binary classification between LOS and NLOS in UWB ranging systems. The MP condition is usually ignored. In fact, the MP condition also has a significant impact on the ranging errors of the UWB compared to the direct LOS measurement results. However, the magnitudes of the error contained in MP conditions are generally lower than completely blocked NLOS scenarios. This paper addresses machine learning techniques for identification of the three mentioned classes (LOS, NLOS, and MP) in the UWB indoor localization system using an experimental dataset. The dataset was collected in different conditions in different scenarios in indoor environments. Using the collected real measurement data, we compared three machine learning (ML) classifiers, i.e., support vector machine (SVM), random forest (RF) based on an ensemble learning method, and multilayer perceptron (MLP) based on a deep artificial neural network, in terms of their performance. The results showed that applying ML methods in UWB ranging systems was effective in the identification of the above-three mentioned classes. Specifically, the overall accuracy reached up to 91.9% in the best-case scenario and 72.9% in the worst-case scenario. Regarding the F1-score, it was 0.92 in the best-case and 0.69 in the worst-case scenario. For reproducible results and further exploration, we provide the publicly accessible experimental research data discussed in this paper at PUB (Publications at Bielefeld University). The evaluations of the three classifiers are conducted using the open-source Python machine learning library scikit-learn.

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“…Consequently, several investigations of NLOS identification process in UWB were examined in the literature using different ML techniques as a classifier such as SVM in [9,10,15,19], MLP in [21,35,36], Boosted decision tree (BDT) in [36], recursive decision tree in [33], and other ML techniques such as kernel principal component analysis in [17], etc. Moreover, the unsupervised machine learning technique called "expectation maximization for Gaussian mixture models" was recently applied in [37] to classify the LOS and NLOS conditions in UWB measurement. Likewise, deep learning approaches such as convolutional neural network (CNN) were also explored to distinguish the NLOS condition from LOS in UWB ranging [20,31].…”

Section: Identification Of Nlos and Mp Conditions In Literature Basedmentioning

confidence: 99%

Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods

Sang¹,

Steinhagen²,

Homburg³

et al. 2020

Preprint

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In Ultra-wideband (UWB)-based wireless ranging or distance measurement, differentiation between line-of-sight~(LOS), non-line-of-sight~(NLOS), and multi-path (MP) conditions are important for precise indoor localization. This is because the accuracy of the reported measured distance in UWB ranging systems is directly affected by the measurement conditions (LOS, NLOS or MP). However, the major contributions in literature only address the binary classification between LOS and NLOS in UWB ranging systems. The MP condition is usually ignored. In fact, the MP condition also has a significant impact on the ranging errors of the UWB compared to the direct LOS measurement results. Though, the magnitudes of the error contained in MP conditions are generally lower than completely blocked NLOS scenarios. This paper addresses machine learning techniques for identification of the mentioned three classes (LOS, NLOS, and MP) in the UWB indoor localization system using an experimental data-set. The data-set was collected in different conditions at different scenarios in indoor environments. Using the collected real measurement data, we compare three machine learning (ML) classifiers, i.e., support vector machine (SVM), random forest (RF) based on an ensemble learning method, and multilayer perceptron (MLP) based on a deep artificial neural network, in terms of their performance. The results show that applying ML methods in UWB ranging systems are effective in identification of the above-mentioned three classes. In specific, the overall accuracy reaches up to 91.9% in the best-case scenario and 72.9% in the worst-case scenario. Regarding the F1-score, it is 0.92 in the best-case and 0.69 in the worst-case scenario. For reproducible results and further exploration, we (will) provide the publicly accessible experimental research data discussed in this paper at PUB - Publications at Bielefeld University. The evaluations of the three classifiers are conducted using the open-source python machine learning library scikit-learn.

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Non-Line-of-Sight Identification Based on Unsupervised Machine Learning in Ultra Wideband Systems

Cited by 54 publications

References 24 publications

A Survey of Machine Learning for Indoor Positioning

A Survey of Machine Learning for Indoor Positioning

Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods

Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods

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