Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/140565
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dc.contributor.authorZhou, Yixinen_US
dc.date.accessioned2020-05-30T14:04:15Z-
dc.date.available2020-05-30T14:04:15Z-
dc.date.issued2020-
dc.identifier.urihttps://hdl.handle.net/10356/140565-
dc.description.abstractNavigation and positioning are the basic needs of human production and life. The invention of the compass, and other tools has promoted economic and social development. Nowadays, the most popular outdoor positioning technology is GNSS and base station positioning of the mobile communication networks. GNSS positioning principle: the navigation satellite continuously broadcasts the navigation message in orbit, the user receives 4 or more satellites, and solves the three-dimensional position and time information. The positioning principle of mobile base station is: measure the downlink pilot signal from the adjacent base station to the user's mobile phone, obtain the signal phase difference PDOA (phase difference of arrival) or arrival time difference TDOA (time difference of arrival), and calculate the user's position with the base station coordinate. The precision of wide range positioning of satellite positioning coverage area is high, but the precision of GNSS signal fading dramatically reduces under the influence of building shelter, which cannot meet the needs of indoor positioning; the precision of indoor positioning of the mobile base station is too low to meet the requirements of precision. People live indoors 80% of the time. In the era of Internet of things, it is urgent to quickly determine the location information of indoor personnel, terminals, important assets, and key objectives in airports, underground parking lots, logistics storage, large supermarkets and other occasions. Therefore, a variety of indoor positioning methods based on infrared, A-GPS, RFID, Bluetooth, ultrasound, optical tracking, ultra-wideband, wireless local area network, pseudo satellite, image analysis computer vision and other technologies have been developed. At present, these technologies have certain limitations, and there is no universal technology to meet the application in various scenarios. So, I decided to find a good method to obtain higher positioning accuracy using the PDOA algorithm. I implemented the algorithm in a simulation environment in MATLAB and enable better range estimation in challenging scenarios.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.relationA3251-191en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleSDR-based PDOA system for indoor localizationen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorTay, Wee Pengen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
dc.contributor.supervisoremailwptay@ntu.edu.sgen_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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