Hlavní stránka > Výsledky hledání: Chalko, Miroslav
Hledej:
Tipy pro vyhledávaní :: Rozšířené hledání
Hledej ve sbírkách:
Seřadit podle: Zobrazit výsledky: Výstupní formát:
Národní úložiště šedé literatury Nalezeno 2 záznamů.  Hledání trvalo 0.00 vteřin. 
Classification of Direct and Reflected Signal Using Embedded System
 Chalko, Miroslav ; Strnadel, Josef (oponent) ; Šimek, Václav (vedoucí práce)
This thesis aims to design and implement an algorithm for classification of signals that are used for tracking objects using ultra-wideband technology. The classification method should be able to detect an obstruction between receiver and transmitter, which means to classify signals as those with line of sight (LOS) and non-line of sight (NLOS). This system must be quick and lightweight enough, so real-time detection can be achieved directly in the embedded system. While searching for the solution, multiple classification methods were examined. The best-performing ones involved numerous variants of decision tree classifiers. Considering the restricted computing power of embedded devices, random forest classifier was chosen as the final solution. This classification method was able to achieve accuracy of up to 89% while evaluating the dataset. When deployed in real-life environment, it was able to detect an object between transmitter and receiver. Classification and calculation of parameters takes 6000 instruction cycles and the algorithm fits into 4kB of memory. Results of this thesis enable improvement of existing solutions for detection of NLOS signals that degrade tracking performance. This will boost the accuracy of localization while tracking objects in indoor environments.
Úplný záznam
Classification of Direct and Reflected Signal Using Embedded System
 Chalko, Miroslav ; Strnadel, Josef (oponent) ; Šimek, Václav (vedoucí práce)
This thesis aims to design and implement an algorithm for classification of signals that are used for tracking objects using ultra-wideband technology. The classification method should be able to detect an obstruction between receiver and transmitter, which means to classify signals as those with line of sight (LOS) and non-line of sight (NLOS). This system must be quick and lightweight enough, so real-time detection can be achieved directly in the embedded system. While searching for the solution, multiple classification methods were examined. The best-performing ones involved numerous variants of decision tree classifiers. Considering the restricted computing power of embedded devices, random forest classifier was chosen as the final solution. This classification method was able to achieve accuracy of up to 89% while evaluating the dataset. When deployed in real-life environment, it was able to detect an object between transmitter and receiver. Classification and calculation of parameters takes 6000 instruction cycles and the algorithm fits into 4kB of memory. Results of this thesis enable improvement of existing solutions for detection of NLOS signals that degrade tracking performance. This will boost the accuracy of localization while tracking objects in indoor environments.
Úplný záznam

Chcete být upozorněni, pokud se objeví nové záznamy odpovídající tomuto dotazu?
Přihlásit se k odběru RSS.
Digitální repozitář :: Hledej :: Nový záznam :: Personalizace :: Nápověda
Powered by Invenio v1.1.2
Spravuje admin.invenio@techlib.cz
Tato stránka je dostupná také v následujících jazycích:
Česky  English