Register now After registration you will be able to apply for this opportunity online.
This opportunity is not published. No applications will be accepted.
Phased array radio localization for behavioral monitoring of birds
Evaluate the potential of using radio localization to greatly simplify our behavioral research on songbirds. Apply machine learning on high-quality datasets with ground truth from the videos to establish a new method.
Keywords: indoor localization, radio emitter localization, software defined radio, animal tracking, behavioral monitoring, ethology
The goal of this master thesis project is to explore the possibility to use the phases and amplitudes of the radio receiver in the four antennas to localize the birds. This would have a tremendous impact on our behavioral research: it would eliminate the need for the costly video recording that produces an enormous amount of data that need to be analyzed by complicated algorithms to track all the birds. With a pure radio tracking the throughput could be enhanced, the costs lowered, and the analyses simplified. We have a large data sets with ground truth from the videos that allows to use machine learning for the mapping of the antenna signals to the position and orientation of the birds. We expect from this project to judge the feasibility of radio tracking for behavioral studies and a measure of the precision of this new and promising method.
The goal of this master thesis project is to explore the possibility to use the phases and amplitudes of the radio receiver in the four antennas to localize the birds. This would have a tremendous impact on our behavioral research: it would eliminate the need for the costly video recording that produces an enormous amount of data that need to be analyzed by complicated algorithms to track all the birds. With a pure radio tracking the throughput could be enhanced, the costs lowered, and the analyses simplified. We have a large data sets with ground truth from the videos that allows to use machine learning for the mapping of the antenna signals to the position and orientation of the birds. We expect from this project to judge the feasibility of radio tracking for behavioral studies and a measure of the precision of this new and promising method.
Your tasks will be literature and data review, implement a machine learning algorithm, assess the performance, and identify critical factors. We expect proper documentation of your code and data, and a useful report that could lead to a publication. We have weekly meetings to discuss outcomes, ideas, and next steps. The thesis workload is designed for 6-month full-time work.
Your tasks will be literature and data review, implement a machine learning algorithm, assess the performance, and identify critical factors. We expect proper documentation of your code and data, and a useful report that could lead to a publication. We have weekly meetings to discuss outcomes, ideas, and next steps. The thesis workload is designed for 6-month full-time work.
Linus Rüttimann: rlinus@ini.ethz.ch
Dr. Jörg Rychen: jrychen@ethz.ch
Prof. Dr. Richard Hahnloser: rich@ini.ethz.ch
Linus Rüttimann: rlinus@ini.ethz.ch Dr. Jörg Rychen: jrychen@ethz.ch Prof. Dr. Richard Hahnloser: rich@ini.ethz.ch
MSC RadioLoc.pdf
