Geolocation with LoRa and machine learning using TDOA algorithm
Date
2021
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Abstract
A vehicle tracking system is a crucial system used in fields such as transportation,
delivery services, and criminal investigations. GPS and GSM system is the most popular
means of determining vehicle location. It is accurate; however, it is costly and consumes a
lot of power due to the need for frequent battery replacement. Implementing it in local public
transportation, especially in African countries is not feasible. This is because installation
and maintenance are expensive.
Various research has been going on to improve geolocation with LoRa. However,
one of the issues that remain is the time accuracy in collecting the time of arrival which is
crucial in determining the vehicle position in the Time of Difference of Arrival (TDOA)
algorithm. This project designed a low-cost and low-power LoRa vehicle tracking system
and researched ways to improve LoRa geolocation using the TDOA algorithm using
machine learning. A multilateration algorithm was used in positioning station gateways for
the TDOA algorithm to provide an accurate measurement, it required a nanoseconds
precision, however, only a millisecond precision was achieved from the hardware setup.
The machine learning model used RandomForest for training and prediction advantages.
The lack of a microcontroller with a nanoseconds precision, limited resources to build a full
multilateration system affected the accuracy of the TDOA algorithm.
Description
Capstone Project submitted to the Department of Engineering, Ashesi University in partial fulfillment of the requirements for the award of Bachelor of Science degree in Computer Engineering, May 2021
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Capstone project
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Keywords
location technology, vehicle tracking