Simulation of dynamic electromagnetic interference environment for Unmanned Aerial Vehicle data link

2013 ◽  
Vol 10 (7) ◽  
pp. 19-28 ◽  
Author(s):  
Guo Shuxia ◽  
Dong Zhongyao ◽  
Hu Zhantao ◽  
Hu Chufeng
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Electromagnetic Interference ◽  
Data Link ◽  
Vehicle Data ◽  
Aerial Vehicle

scholarly journals Studies on Novel Anti-jamming Technique of Unmanned Aerial Vehicle Data Link

2008 ◽  
Vol 21 (2) ◽  
pp. 141-148 ◽  
Author(s):  
Huang Wenzhun ◽  
Wang Yongsheng ◽  
Ye Xiangyang
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Data Link ◽  
Vehicle Data ◽  
Aerial Vehicle

scholarly journals How Accurate Is an Unmanned Aerial Vehicle Data-Based Model Applied on Satellite Imagery for Chlorophyll-a Estimation in Freshwater Bodies?

2021 ◽  
Vol 13 (6) ◽  
pp. 1134
Author(s):  
Anas El-Alem ◽  
Karem Chokmani ◽  
Aarthi Venkatesan ◽  
Lhissou Rachid ◽  
Hachem Agili ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Unmanned Aerial Vehicle ◽  
Optical Sensors ◽  
Regional Scale ◽  
Turbid Waters ◽  
Vehicle Data ◽  
Freshwater Bodies ◽  
Aerial Vehicle ◽  
Leave One Out ◽  
Semi Empirical

Optical sensors are increasingly sought to estimate the amount of chlorophyll a (chl_a) in freshwater bodies. Most, whether empirical or semi-empirical, are data-oriented. Two main limitations are often encountered in the development of such models. The availability of data needed for model calibration, validation, and testing and the locality of the model developed—the majority need a re-parameterization from lake to lake. An Unmanned aerial vehicle (UAV) data-based model for chl_a estimation is developed in this work and tested on Sentinel-2 imagery without any re-parametrization. The Ensemble-based system (EBS) algorithm was used to train the model. The leave-one-out cross validation technique was applied to evaluate the EBS, at a local scale, where results were satisfactory (R2 = Nash = 0.94 and RMSE = 5.6 µg chl_a L−1). A blind database (collected over 89 lakes) was used to challenge the EBS’ Sentine-2-derived chl_a estimates at a regional scale. Results were relatively less good, yet satisfactory (R2 = 0.85, RMSE= 2.4 µg chl_a L−1, and Nash = 0.79). However, the EBS has shown some failure to correctly retrieve chl_a concentration in highly turbid waterbodies. This particularity nonetheless does not affect EBS performance, since turbid waters can easily be pre-recognized and masked before the chl_a modeling.

scholarly journals Remote Estimation of Vegetation Fraction and Flower Fraction in Oilseed Rape with Unmanned Aerial Vehicle Data

2016 ◽  
Vol 8 (5) ◽  
pp. 416 ◽  
Author(s):  
Shenghui Fang ◽  
Wenchao Tang ◽  
Yi Peng ◽  
Yan Gong ◽  
Can Dai ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Oilseed Rape ◽  
Vegetation Fraction ◽  
Vehicle Data ◽  
Aerial Vehicle ◽  
Remote Estimation

scholarly journals Research Progress of Anti-jamming Technology of Unmanned Aerial Vehicle(UAV) Data Link

2020 ◽  
Vol 816 ◽  
pp. 012011
Author(s):  
Guo Shichao ◽  
Guo Dandan ◽  
Zhang Qiongyu ◽  
Wu Nankai ◽  
Deng Jiaxin
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Research Progress ◽  
Data Link ◽  
Aerial Vehicle

scholarly journals Mechanical Harvesting Effectively Controls Young Typha spp. Invasion and Unmanned Aerial Vehicle Data Enhances Post-treatment Monitoring

2017 ◽  
Vol 8 ◽  
Author(s):  
Shane C. Lishawa ◽  
Brendan D. Carson ◽  
Jodi S. Brandt ◽  
Jason M. Tallant ◽  
Nicholas J. Reo ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Treatment Monitoring ◽  
Post Treatment ◽  
Vehicle Data ◽  
Aerial Vehicle

Characterizing Electromagnetic Interference Signals for Unmanned Aerial Vehicle Geophysical Surveys

Geophysics ◽  
2021 ◽  
pp. 1-71
Author(s):  
Callum Walter ◽  
Alexander Braun ◽  
Georgia Fotopoulos
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Electromagnetic Interference ◽  
High Frequency ◽  
Rotation Frequency ◽  
Mitigation Strategies ◽  
Fluxgate Magnetometer ◽  
Geophysical Surveys ◽  
Aerial Vehicle ◽  
Interference Signals ◽  
Electromagnetic Signals

The development of a functional Unmanned Aerial Vehicle (UAV) mounted aeromagnetic system requires integrating a magnetometer payload onboard a UAV platform in a manner that preserves the integrity of the total magnetic field measurements. One challenge when developing these systems is accounting for the sources of in-flight magnetic and electromagnetic interference signals that are greater than the resolvability threshold of the magnetometer. Electromagnetic interference generated by the platform has the potential to be mitigated using several techniques such as magnetic shielding, filtering, or compensation; and can be attenuated by strategically positioning the magnetometer at a distance from the UAV. The integration procedure and selection of a mitigation strategy can be informed by characterizing the electromagnetic interference generated by the platform. Scalogram analysis was employed to characterize the high-frequency electromagnetic signals generated by a multi-rotor UAV’s electromagnetic motors. A low sensitivity (7 nT) vector, fluxgate magnetometer was used to measure the electromagnetic interference generated by two unique multi-rotor UAVs in a controlled lab setting. Results demonstrated three spectrally distinct electromagnetic signals, each with unique frequency and amplitude, generated by each UAV platform. The frequency of these electromagnetic interference signals was found to be directly proportional to the applied rotation frequency of the electromagnetic motor. The aforementioned knowledge was applied to UAV field surveys to assess the high-frequency electromagnetic interference signals experienced. This was achieved by using a high sensitivity (0.01 nT), scalar optically pumped magnetometer with a 1000 Hz sampling frequency. The results show that adequate sensor placement and pre-flight evaluation of the platform-sensor interactions provide useful mitigation strategies, which can compensate for electromagnetic interference signals generated by the UAV platform during aeromagnetic surveys.

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