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Start Over Descriptor "NLOS detection" Topic uwb
4 results on '"NLOS detection"'

1. Environmental Cross-Validation of NLOS Machine Learning Classification/Mitigation with Low-Cost UWB Positioning Systems

Author

Carlos Escudero, Jose A. Garcia-Naya, Pedro Suarez-Casal, and Valentín Barral

Subjects
Indoor location algorithms, Computer science, Real-time computing, NLOS detection, 02 engineering and technology, 01 natural sciences, Biochemistry, Article, Cross-validation, Analytical Chemistry, Non-line-of-sight propagation, UWB, Position (vector), Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Electrical and Electronic Engineering, Instrumentation, indoor location algorithms, 010401 analytical chemistry, 020206 networking & telecommunications, Ranging, neural networks, Atomic and Molecular Physics, and Optics, 0104 chemical sciences, Statistical classification, machine learning, Face (geometry), Radio frequency, Neural networks
Abstract

[Abstract] Indoor positioning systems based on radio frequency inherently present multipath-related phenomena. This causes ranging systems such as ultra-wideband (UWB) to lose accuracy when detecting secondary propagation paths between two devices. If a positioning algorithm uses ranging measurements without considering these phenomena, it will face critical errors in estimating the position. This work analyzes the performance obtained in a localization system when combining location algorithms with machine learning techniques applied to a previous classification and mitigation of the propagation effects. For this purpose, real-world cross-scenarios are considered, where the data extracted from low-cost UWB devices for training the algorithms come from a scenario different from that considered for the test. The experimental results reveal that machine learning (ML) techniques are suitable for detecting non-line-of-sight (NLOS) ranging values in this situation. Xunta de Galicia; ED431C 2016-045 Xunta de Galicia; ED431G/01 Agencia Estatal de Investigación de España; TEC2016-75067-C4-1-R

Published
2019
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2. Environmental Cross-Validation of NLOS Machine Learning Classification/Mitigation with Low-Cost UWB Positioning Systems.

Author

Barral, Valentín, Escudero, Carlos J., García-Naya, José A., and Suárez-Casal, Pedro

Subjects
*INDOOR positioning systems, *MACHINE learning, *RADIO frequency, *JOB performance
Abstract

Indoor positioning systems based on radio frequency inherently present multipath-related phenomena. This causes ranging systems such as ultra-wideband (UWB) to lose accuracy when detecting secondary propagation paths between two devices. If a positioning algorithm uses ranging measurements without considering these phenomena, it will face critical errors in estimating the position. This work analyzes the performance obtained in a localization system when combining location algorithms with machine learning techniques applied to a previous classification and mitigation of the propagation effects. For this purpose, real-world cross-scenarios are considered, where the data extracted from low-cost UWB devices for training the algorithms come from a scenario different from that considered for the test. The experimental results reveal that machine learning (ML) techniques are suitable for detecting non-line-of-sight (NLOS) ranging values in this situation. [ABSTRACT FROM AUTHOR]

Published
2019
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3. A decision tree‐based NLOS detection method for the UWB indoor location tracking accuracy improvement.

Author

Musa, Ardiansyah, Nugraha, Gde Dharma, Han, Hyojeong, Choi, Deokjai, Seo, Seongho, and Kim, Juseok

Subjects
*DECISION trees, *ARTIFICIAL satellite tracking, *TRACKING algorithms, *IMMUNITY
Abstract

Summary: Among existing wireless technologies, ultra‐wideband (UWB) is the most promising solution for indoor location tracking. UWB has a great multipath fading immunity; however, great multipath resolvability alone does not eliminate the effect of non‐line‐of‐sight (NLOS) and multipath propagation. NLOS and multipath propagation in indoor environments can easily produce meters of UWB ranging error. This condition gives an enormous impact on the accuracy of indoor location tracking data. To address this problem, we propose an NLOS detection method using recursive decision tree learning. Using the UWB channel quality indicators information, we develop our model with the Gini index and altered priors splitting criteria. We then validate the constructed model using the 10‐fold cross‐validation method. Our experiment shows that the constructed model has correctly detected 90% of both line‐of‐sight (LOS) and NLOS cases on the seven different indoor environments. The result of this work can be used for the UWB indoor location tracking accuracy improvement. [ABSTRACT FROM AUTHOR]

Published
2019
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4. Innovative Signal Processing Techniques for Wireless Positioning

Author

Davide Dardari, Mario Di Dio, Andrea Emmanuele, Diana Fontanella, Sinan Gezici, Mohammad Reza Gholami, Michel Kieffer, Eva Lagunas, Jérôme Louveaux, Achraf Mallat, Montse Nájar, Monica Navarro, Monica Nicoli, Luca Reggiani, Mats Rydström, Erik G. Ström, Luc Vandendorpe, Francesca Zanier, Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions, and Universitat Politècnica de Catalunya. A&MP - Grup de Processament d'Arrays i Sistemes Multicanal

Subjects
Signal processing, Optimization problem, Positioning system, Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors [Àrees temàtiques de la UPC], Cognitive positioning, Real-time computing, MIMO, education, TOA/AOA estimation, NLOS detection, Radio spectrum, POCS, Interference (communication), UWB, Electronic engineering, TOA estimation in NLOS conditions, Frequency-domain TOA estimation, Estimator, Cooperative positioning, Tractament del senyal, Enginyeria de la telecomunicació::Processament del senyal [Àrees temàtiques de la UPC], Wireless communication systems, Geography, Cognitive radio, Comunicació sense fil, Sistemes de
Abstract

Publisher Summary This chapter presents a collection of the latest research results in the field of wireless positioning carried out within the European Network of Excellence NEWCOMCC. One of the most recent promising topics is cognitive positioning (CP) in which the main idea is to design the positioning system according to the cognitive radio (CR) paradigm with the purpose to use efficiently the radio spectrum and to optimize the system performance in the presence of interference. The algorithm needs a measure of the TOA at each array element to obtain the final joint TOA and AOA estimation. The frequency samples are processed without the knowledge of the specific pulse shape in the fine TOA estimator stage. The fine estimator requires a previous stage that provides a coarse symbol synchronization. In localization systems, a characteristic signal is detected and measured by a set of sensors with fixed or known positions, and the positioning problem is then often formulated as a weighted least-squares (WLS) optimization problem.

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