Authors:
Gianluca Moro
1
;
Roberto Pasolini
1
and
Davide Dardari
2
Affiliations:
1
Department of Computer Science and Engineering - DISI, University of Bologna, Via dell'Università 50, I-47522 Cesena (FC) and Italy
;
2
Department of Electrical, Electronic and Information Engineering – DEI, University of Bologna, Via dell'Università 50, I-47522 Cesena (FC) and Italy
Keyword(s):
Ultra-Wide Band, Localization, Non-Line-Of-Sight Identification, Data Mining, Machine Learning.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Business Analytics
;
Data Engineering
;
Data Management and Quality
;
Data Manipulation
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Human-Computer Interaction
;
Management of Sensor Data
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Predictive Modeling
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
Localisation algorithms based on the estimation of the time-of-arrival of the received signal are particularly interesting when ultra-wide band (UWB) signaling is adopted for high-definition location aware applications. In this context non-line-of-sight (NLOS) propagation condition may drastically degrade the localisation accuracy if not properly recognised. We propose a new NLOS identification technique based on the analysis of UWB signals through supervised and unsupervised machine learning algorithms, which are typically adopted to extract knowledge from data according to the data mining approach. Thanks to these algorithms we can automatically generate a very reliable model that recognises if an UWB received signal has crossed obstacles (NLOS situation). The main advantage of this solution is that it extracts the model for NLOS identification directly from example waveforms gathered in the environment and does not rely on empirical tuning of parameters as required by other NLOS i
dentification algorithms. Moreover experiments show that accurate NLOS classifiers can be extracted from measured signals either pre-classified or unclassified and even from samples algorithmically-generated from statistical models, allowing the application of the method in real scenarios without training it on real data.
(More)