Print Email Facebook Twitter Investigation on Time-of-Arrival Estimationfor the LoRa Network Title Investigation on Time-of-Arrival Estimationfor the LoRa Network Author DAI, Ming (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Circuits and Systems) Contributor van der Veen, A.J. (mentor) Janssen, G.J.M. (graduation committee) Irahhauten, Z. (graduation committee) Pawełczak, Przemysław (graduation committee) Kazaz, T. (graduation committee) Degree granting institution Delft University of Technology Programme Electrical Engineering | Circuits and Systems Date 2019-03-26 Abstract LoRa (Long Range) is a low-power, long-range and low-cost wireless communication system that can facilitate a wide variety of infrastructures for the Internet of Things (IoT). Current algorithms to locate LoRa tags have a resolution of 100 m in practice, and a question is if that can be improved without changing the tags or adding too much to the gateways (basestations). Conventional delay estimation ranging algorithms extract useful information from the channel frequency response and use this information to estimate delays. In this thesis, three localization techniques are presented: the matched filter, FBCM-MUSIC and TLS-ESPRIT algorithms. Then a multiband architecture is proposed and integrated into the matched filter. These algorithms are implemented in the LoRa system model. The simulations indicate that FBCM-MUSIC and TLS-ESPRIT have better performance than the matched filter in NLOS channels. The results also show that TLS-ESPRIT is more effective and robust compared to MUSIC. The proposed multiband architecture can improve the resolution of TOA estimation and decreases the 90th percentile error by around 40%. Subject TOA EstimationLoRa NetworkHigh Resolution To reference this document use: http://resolver.tudelft.nl/uuid:397d5a3f-ff07-47d9-b645-931f049cf983 Part of collection Student theses Document type master thesis Rights © 2019 Ming DAI Files PDF thesis_LoRa.pdf 2.07 MB Close viewer /islandora/object/uuid:397d5a3f-ff07-47d9-b645-931f049cf983/datastream/OBJ/view