Study of demodulation technology of angle-error in unmanned aerial vehicle data-link

2015 ◽  
pp. 1633-1636
Author(s):  
Jingtao Sun
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Angle Error ◽  
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

scholarly journals A Vision-Based Approach to UAV Detection and Tracking in Cooperative Applications

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3391 ◽  
Author(s):  
Roberto Opromolla ◽  
Giancarmine Fasano ◽  
Domenico Accardo
Keyword(s):  
Template Matching ◽  
Ad Hoc ◽  
False Alarms ◽  
Data Link ◽  
Detection And Tracking ◽  
Vehicle Data ◽  
Link Performance ◽  
Wide Range ◽  
Aerial Vehicle ◽  
Uav Navigation

This paper presents a visual-based approach that allows an Unmanned Aerial Vehicle (UAV) to detect and track a cooperative flying vehicle autonomously using a monocular camera. The algorithms are based on template matching and morphological filtering, thus being able to operate within a wide range of relative distances (i.e., from a few meters up to several tens of meters), while ensuring robustness against variations of illumination conditions, target scale and background. Furthermore, the image processing chain takes full advantage of navigation hints (i.e., relative positioning and own-ship attitude estimates) to improve the computational efficiency and optimize the trade-off between correct detections, false alarms and missed detections. Clearly, the required exchange of information is enabled by the cooperative nature of the formation through a reliable inter-vehicle data-link. Performance assessment is carried out by exploiting flight data collected during an ad hoc experimental campaign. The proposed approach is a key building block of cooperative architectures designed to improve UAV navigation performance either under nominal GNSS coverage or in GNSS-challenging environments.

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