Migrating Monarch Butterfly Localization Using Multi-Sensor Fusion Neural Networks
Abstract
Details of Monarch butterfly migration from the U.S. to Mexico remain a mystery due to lack of a proper localization technology to accurately localize and track butterfly migration. In this paper, we propose a deep learning based butterfly localization algorithm that can estimate a butterfly's daily location by analyzing a light and temperature sensor data log continuously obtained from an ultra-low power, mm-scale sensor attached to the butterfly. To train and test the proposed neural network based multi-sensor fusion localization algorithm, we collected over 1500 days of real world sensor measurement data with 82 volunteers all over the U.S. The proposed algorithm exhibits a mean absolute error of <1.5 degree in latitude and <0.5 degree in longitude Earth coordinate, satisfying our target goal for the Monarch butterfly migration study.
Cite
@article{arxiv.1912.06907,
title = {Migrating Monarch Butterfly Localization Using Multi-Sensor Fusion Neural Networks},
author = {Mingyu Yang and Roger Hsiao and Gordy Carichner and Katherine Ernst and Jaechan Lim and Delbert A. Green and Inhee Lee and David Blaauw and Hun-Seok Kim},
journal= {arXiv preprint arXiv:1912.06907},
year = {2019}
}
Comments
under review for ICASSP 2020