A noise robust automatic radiolocation animal tracking system

Animal Biotelemetry

Greg J. Bishop-Hurley¹  Ed Charmley²  Liang Wang³  Foivos Diakogiannis⁴  Nigel Bajema⁵  Scott Mills⁵  Ian Atkinson⁵ 

[1] Agriculture and Food, CSIRO, 4067, Brisbane, Australia;Agriculture and Food, CSIRO, 4814, Townsville, Australia;Agriculture and Food, CSIRO, 4814, Townsville, Australia;Agriculture and Food, CSIRO, 4067, Brisbane, Australia;ICRAR, The University of Western Australia, 6009, Perth, Australia;Data61, CSIRO, 6014, Perth, Australia;eResearch Centre, James Cook University, 4814, Townsville, Australia;

关键词: Radiolocation;    Machine learning;    Encoder/decoder;    Convolutional neural network;   

DOI  :  10.1186/s40317-021-00248-w

来源: Springer

【 摘 要 】

Agriculture is becoming increasingly reliant upon accurate data from sensor arrays, with localization an emerging application in the livestock industry. Ground-based time difference of arrival (TDoA) radio location methods have the advantage of being lightweight and exhibit higher energy efficiency than methods reliant upon Global Navigation Satellite Systems (GNSS). Such methods can employ small primary battery cells, rather than rechargeable cells, and still deliver a multi-year deployment. In this paper, we present a novel deep learning algorithm adapted from a one-dimensional U-Net implementing a convolutional neural network (CNN) model, originally developed for the task of semantic segmentation. The presented model (ResUnet-1d) both converts TDoA sequences directly to positions and reduces positional errors introduced by sources such as multipathing. We have evaluated the model using simulated animal movements in the form of TDoA position sequences in combination with real-world distributions of TDoA error. These animal tracks were simulated at various step intervals to mimic potential TDoA transmission intervals. We compare ResUnet-1d to a Kalman filter to evaluate the performance of our algorithm to a more traditional noise reduction approach. On average, for simulated tracks having added noise with a standard deviation of 50 m, the described approach was able to reduce localization error by between 66.3% and 73.6%. The Kalman filter only achieved a reduction of between 8.0% and 22.5%. For a scenario with larger added noise having a standard deviation of 100 m, the described approach was able to reduce average localization error by between 76.2% and 81.9%. The Kalman filter only achieved a reduction of between 31.0% and 39.1%. Results indicate that this novel 1D CNN U-Net like encoder/decoder for TDoA location error correction outperforms the Kalman filter. It is able to reduce average localization errors to between 16 and 34 m across all simulated experimental treatments while the uncorrected average TDoA error ranged from 55 to 188 m.

【 授权许可】

CC BY