Integration of 5.8GHz Doppler Radar and Machine Learning for Automated Honeybee Hive Surveillance and Logging

被引：4

作者：

Aldabashi, Nawaf ^{[1
]}

Williams, Sam ^{[1
]}

Eltokhy, Amira ^{[2
]}

Palmer, Edward ^{[3
]}

Cross, Paul ^{[1
]}

Palego, Cristiano ^{[1
]}

机构：

[1] Bangor Univ, Bangor, Gwynedd, Wales

[2] MSA Univ, Cairo, Egypt

[3] S&A Produce Ltd, Hereford, England

来源：

2021 IEEE MTT-S INTERNATIONAL MICROWAVE SYMPOSIUM (IMS) | 2021年

关键词：

Bistatic radar; Doppler radar; Radar cross-sections; Simulation; Machine learning;

D O I：

10.1109/IMS19712.2021.9574826

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A 5.8GHz Doppler radar was used to monitor free flying honeybees entering and leaving their hive at a 2m distance. Free falling metal spheres of different size and materials were first used, along with radar cross section (RCS) simulations, for calibration of an in house continuous-wave (CW) radar system. The system was then applied to extract the RCS of free flying honeybees (n=164) at 5.8GHz, which fills a gap in the literature and was found to be in the range of -55 to -60dBsm +/- 3dBsm. The Doppler radar was hence integrated with machine learning (ML) techniques to autonomously discriminate the incoming and outgoing flights of honeybees. A neural network built through a random forest algorithm and processing of the data as Line Spectral Pairs (LSPs) achieved a maximum accuracy of 87.83% with a Binary Cross Entropy loss of 0.4274 when interpreting hive departure/entrance events.

引用

页码：625 / 628

页数：4

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