Optimization of a multi-cycle remote sensing system using UAVS
DOI: 10.34759/trd-2023-129-22
Аuthors
National Aerospace Agency of Azerbaijan Republic, NASA, 1, Suleyman Sani Akhundov str., Baku, AZ1115, Azerbaijan Republic
e-mail: h.dilan@mail.ru
Abstract
The subject of the study is multi-cycle remote sensing systems, particularly, the optimal multi-cycle remote sensing systems based on unmanned aerial vehicles (UAVs). Analysis and synthesis of a multi-cycle remote sensing mode is being realized by measuring equipment installed on the UAV. Two optimization problems are considered and solved. In the first problem, measurement cycles in the number of n differ in the fact that the flight altitude in every cycle is different, and the problem of determining the optimal relationship between the flight altitude and the cycle duration is being set. The second optimization problem envisages computing the optimal dependence of the cycle duration on the length of the distance traveled during one cycle.
As the result of solving the first problem, the author shows that a specially formed functional of the target, called the «UAV flight area», will reach its minimum if the flight altitude is decreasing so far as the duration of the measurement cycle is increasing. The result of the second problem solution reveals the amount of information obtained in multi-cycle mode reaches its maximum value given a direct interrelation between the said indicators.
The results obtained in this work may be applied while the development and application of remote sensing systems, based on the unmanned aerial vehicles, operating in a multi-cycle mode.
The main inference of the study consists in the fact that remote sensing systems operating in multi-cycle mode may be optimized by various criteria. The optimization criterion selection herewith depends on the purpose set for the concrete realization of the remote sensing system based on the UAV.
Keywords:
multicycle mode, remote sensing, unmanned aerial vehicle, optimization, flight altitudeReferences
- Pena F., Luna P., Isaac M., Ragab A. R., Elmenshawy K., Gomez D., Campoy P., Molina M. A proposed system for multi-UAVs in remote sensing operations, Sensors, 2022, vol. 22, pp. 9180. URL: https://doi.org/10.3390/s22239180
- Yao H., Qin R., Chen X. Unmanned aerial vecihle for remite sensing applications- A review, Remote Sensing, 2019, vol. 11, pp. 1443. DOI: 10.3390/rs11121443
- Karimov A.Kh. Trudy MAI, 2011, no. 47. URL: https://trudymai.ru/eng/published.php?ID=26769
- Karimov A.Kh. Trudy MAI, 2011, no. 47. URL: https://trudymai.ru/eng/published.php?ID=26767
- Karimov A.Kh. Trudy MAI, 2011, no. 47. URL: https://trudymai.ru/eng/published.php?ID=26768
- Adamtsevich L.A., Vorob’ev P.Yu., Zheleznov E.M. Stroitel’stvo i arkhitektura, 2021, vol. 9, no. 3, pp. 51-55. DOI: 10.29039/2308-0191-2021-9-3-51-55
- Dolgopolov D.V. Vestnik Sibirskogo gosudarstvennogo universiteta geosistem i tekhnologii, 2020, vol. 25, no. 4, pp. 85-95. DOI: 10.33764/2411-1759-2020-25-4-85-95
- Studenikin A.V., Mikhalin V.A., Ivanov R.V., Magarshak S.I. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2012, vol. 9, no. 4, pp. 102-106.
- Ivanova Yu.N., Ivanov K.S., Bondareva M.K., Ivanov I.G., Zhukov A.O. Issledovanie Zemli iz kosmosa, 2021, no. 1, pp. 78-88. DOI: 10.31857/S0205961421010061
- Dubyanskii V.M., Tsapko N.V., Shaposhnikova L.I., Degtyarev D.Yu., Davydova N.A. et al. Zdorov’e naseleniya i sreda obitaniya-ZNISO, 2018, no. 2, pp. 52-56. DOI: 10.35627/2219-5238/2018-299-2-52-56
- Ainakulov Zh.Zh., Makarenko N.G., Paltashev T.T. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, vol. 15, no. 7, pp. 43-50. DOI: 10.21046/2070-7401-2018-15-7-43-50
- Kupryashina D.S., Bazarova M.T., Tyuklenkova E.P. Sovremennye nauchnye issledovaniya i innovatsii, 2018, no. 6. URL: https://web.snauka.ru/issues/2018/06/86696
- Ahmed M., Mumtaz R., Anwar Z., Shaukat A., Arif O., Shafait F. Amulti-step approach or optically active and inactive water quality parameter estimation using deep learning and remote sensing, Water, 2022, vol. 14, pp. 2112. DOI: 10.3390/w14132112
- Yan J., Chen X., Chen Y., Liang D. Multistep prediction of land cover from dense time series remote sensing images with temporal convolutional networks, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, vol. 13. URL: https://ieeexplore.ieee.org/document/9184116
- Papadomanolaki M., Christodoulidis S., Karantzalos K., Vakalopoulou M. Unsupervised multistep deformable registration of remote sensing imagery based on deep learning, Remote Sensing, 2021, vol. 13, pp. 1294. DOI: 10.3390/rs13071294
- Zhang Y., Yang Y., Zhang Q., Duan R., Liu J., Qin Y., Wang X. Toward multi-stage phenotyping of soybean with multimodal UAV sensor data: a comparison of machine learning approaches for leaf aera index estimation, Remote Sensing, 2023, vol. 15. DOI: 10.3390/rs13071294
- Polonelli T., Qin Y., Yeatman E. M., Benini L., Boyle D. A flexible, Low-power platform for UAV-based data collection from remote sensors, IEEE Access, 2020, pp. 3021370. DOI:10.1109/ACCESS.2020.3021370
- Pranchai A., Zonklin N., Sirirueang K. A comparative evaluation of unmanned aerial vehicles (UAVs) for forest survey, Journal of Tropical Forest Research, 2019, 3(1), 54-61.
- Liu C., Akbar A., Wu H. Dynamic model constrained optimal flight speed determination of surveying UAV under wind condition, 26th International Conference on Geoinformatics, 2018. DOI:10.1109/GEOINFORMATICS.2018.8557071
- El’gol’ts L.E. Differentsial’nye uravneniya i variatsionnoe ischislenie (Differential equations and calculus of variations), Moscow, Nauka, 1974, 472 p.
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