GNSS multipath rejection based on environment knowledge
Ozouf, Clément (2018)
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Ozouf, Clément
Åbo Akademi
2018
Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2018111548217
https://urn.fi/URN:NBN:fi-fe2018111548217
Tiivistelmä
The idea of an autonomous car has long been a dream. Now, thanks to new technologies coming on the market this dream is becoming reality. In the same was as Star Trek imagined 3D printing, Knight Rider featured an example of an autonomous car.
Currently in this age of autonomous devices, many companies like Waymo and Uber are developing their own autonomous cars using different specificities and development ideas.
Having a suitable and reliable system capable of moving safely on the roads of the world is now possible by combining different environmental information. A majority of systems use GNSS, LiDAR, IMU amongst others to obtain a save device which meets the safety integrity level (SIL) standards.
The project CAMPUS is split due to the complexity of an autonomous car project.
One part of the problem is the positioning of the device. It is common to use Global Navigation Satellite System (GNSS), even if GNSS suffers from signals properties drawbacks such as reflection or refraction, transmission, absorption and cause multipath effects.
One of GNSS’s major issues is its capacity to locate a car inside an urban canyon with an acceptable level of accuracy. Therefore, the focus of my thesis is to develop a software solution based on a multipath rejection algorithm, which has knowledge of the environment.
With the reused of this algorithm, SAFRAN targets one of the best positioning system for autonomous car and the first targeted project is CAMPUS.
Currently in this age of autonomous devices, many companies like Waymo and Uber are developing their own autonomous cars using different specificities and development ideas.
Having a suitable and reliable system capable of moving safely on the roads of the world is now possible by combining different environmental information. A majority of systems use GNSS, LiDAR, IMU amongst others to obtain a save device which meets the safety integrity level (SIL) standards.
The project CAMPUS is split due to the complexity of an autonomous car project.
One part of the problem is the positioning of the device. It is common to use Global Navigation Satellite System (GNSS), even if GNSS suffers from signals properties drawbacks such as reflection or refraction, transmission, absorption and cause multipath effects.
One of GNSS’s major issues is its capacity to locate a car inside an urban canyon with an acceptable level of accuracy. Therefore, the focus of my thesis is to develop a software solution based on a multipath rejection algorithm, which has knowledge of the environment.
With the reused of this algorithm, SAFRAN targets one of the best positioning system for autonomous car and the first targeted project is CAMPUS.