Sensor Data Adaptive Treatment in Time-Varying UAV Medium: Approaches and Methods
Received: 5 November 2025 | Revised: 15 December 2025 | Accepted: 26 December 2025 | Online: 4 April 2026
Corresponding author: Dmytro Borovyk
Abstract
Unmanned Aerial Vehicles (UAVs) that can navigate independently in a variety of situations are in high demand. Thus, distinct UAV configurations that incorporate a range of components, including communication devices, navigation sensors, and additional payloads, are needed, necessitating the development of action models which take into account the dynamic environment and operational goals while limiting the use of resources. The current study presents an integrative review of approaches, methods, concerns, and prospects in the field of adaptive sensor data treatment in a time-varying UAV environment. It provides a comprehensive overview of UAV route planning approaches, such as integrated algorithmic frameworks, biologically inspired approaches, stochastic sampling techniques, and deterministic models. The literature review revealed that sensing accuracy directly affects performance, while substantial research has been conducted to provide trustworthy sensing data to guarantee normal flying. Moreover, it is believed that the prediction accuracy of current filter-based statistical models is independent of the flying conditions. This research highlights the effectiveness of recent data-driven adaptive fusion state-space models in measuring the prediction uncertainty of the system model to prevent degradation in the performance estimation. The study also reviews techniques for adjusting the noise parameter of the statistical model based on the estimated variance, depending on the multi-output Gaussian Process Regression (GPR). The sparse GPR model is proposed for incorporating available characteristics and achieving high estimation accuracy under dynamic operating situations.
Keywords:
UAV control system, unscented Kalman filter, autonomous navigation, Gaussian process regressionDownloads
References
I. Al-Darraji et al., "A Technical Framework for Selection of Autonomous UAV Navigation Technologies and Sensors," Computers, Materials & Continua, vol. 68, no. 2, pp. 2771–2790, 2021. DOI: https://doi.org/10.32604/cmc.2021.017236
B. Hu and P. Seiler, "Pivotal Decomposition for Reliability Analysis of Fault Tolerant Control Systems on Unmanned Aerial Vehicles," Reliability Engineering & System Safety, vol. 140, pp. 130–141, Aug. 2015. DOI: https://doi.org/10.1016/j.ress.2015.04.005
H.-J. Ma, Y. Liu, T. Li, and G.-H. Yang, "Nonlinear High-Gain Observer-Based Diagnosis and Compensation for Actuator and Sensor Faults in a Quadrotor Unmanned Aerial Vehicle," IEEE Transactions on Industrial Informatics, vol. 15, no. 1, pp. 550–562, Jan. 2019. DOI: https://doi.org/10.1109/TII.2018.2865522
A. White and A. Karimoddini, "Event-Based Diagnosis of Flight Manoeuvres of a Fixed-Wing Aircraft," Reliability Engineering & System Safety, vol. 193, Jan. 2020, Art. no. 106609. DOI: https://doi.org/10.1016/j.ress.2019.106609
I. H. Imran, K. Wood, and A. Montazeri, "Adaptive Control of Unmanned Aerial Vehicles with Varying Payload and Full Parametric Uncertainties," Electronics, vol. 13, no. 2, Jan. 2024, Art. no. 347. DOI: https://doi.org/10.3390/electronics13020347
G. Di Rito and F. Schettini, "Impacts of Safety on the Design of Light Remotely-Piloted Helicopter Flight Control Systems," Reliability Engineering & System Safety, vol. 149, pp. 121–129, May 2016. DOI: https://doi.org/10.1016/j.ress.2015.12.012
C. Kanellakis and G. Nikolakopoulos, "Survey on Computer Vision for UAVs: Current Developments and Trends," Journal of Intelligent & Robotic Systems, vol. 87, no. 1, pp. 141–168, July 2017. DOI: https://doi.org/10.1007/s10846-017-0483-z
A. Al-Kaff, D. Martín, F. García, A. D. L. Escalera, and J. María Armingol, "Survey of Computer Vision Algorithms and Applications for Unmanned Aerial Vehicles," Expert Systems with Applications, vol. 92, pp. 447–463, Feb. 2018. DOI: https://doi.org/10.1016/j.eswa.2017.09.033
M. Y. Arafat and S. Moh, "Bio-Inspired Approaches for Energy-Efficient Localization and Clustering in UAV Networks for Monitoring Wildfires in Remote Areas," IEEE Access, vol. 9, pp. 18649–18669, 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3053605
Y. Wang, H. Wang, B. Liu, Y. Liu, J. Wu, and Z. Lu, "A Visual Navigation Framework for the Aerial Recovery of UAVs," IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–13, 2021. DOI: https://doi.org/10.1109/TIM.2021.3126398
J. Gaigalas, L. Perkauskas, H. Gricius, T. Kanapickas, and A. Kriščiūnas, "A Framework for Autonomous UAV Navigation Based on Monocular Depth Estimation," Drones, vol. 9, no. 4, Mar. 2025, Art. no. 236. DOI: https://doi.org/10.3390/drones9040236
C. Iamsumang, A. Mosleh, and M. Modarres, "Monitoring and Learning Algorithms for Dynamic Hybrid Bayesian Network in Online System Health Management Applications," Reliability Engineering & System Safety, vol. 178, pp. 118–129, Oct. 2018. DOI: https://doi.org/10.1016/j.ress.2018.05.016
H. Jeong, B. Park, S. Park, H. Min, and S. Lee, "Fault Detection and Identification Method Using Observer-Based Residuals," Reliability Engineering & System Safety, vol. 184, pp. 27–40, Apr. 2019. DOI: https://doi.org/10.1016/j.ress.2018.02.007
E. D’Amato, M. Mattei, I. Notaro, and V. Scordamaglia, "UAV Sensor FDI in Duplex Attitude Estimation Architectures Using a Set-Based Approach," IEEE Transactions on Instrumentation and Measurement, vol. 67, no. 10, pp. 2465–2475, Oct. 2018. DOI: https://doi.org/10.1109/TIM.2018.2838718
N. Li, N. Gebraeel, Y. Lei, L. Bian, and X. Si, "Remaining Useful Life Prediction of Machinery Under Time-Varying Operating Conditions Based on a Two-Factor State-space Model," Reliability Engineering & System Safety, vol. 186, pp. 88–100, June 2019. DOI: https://doi.org/10.1016/j.ress.2019.02.017
K. Guo, Z. Ye, D. Liu, and X. Peng, "UAV Flight Control Sensing Enhancement with a Data-Driven Adaptive Fusion Model," Reliability Engineering & System Safety, vol. 213, Sept. 2021, Art. no. 107654. DOI: https://doi.org/10.1016/j.ress.2021.107654
M. Y. Arafat, M. M. Alam, and S. Moh, "Vision-Based Navigation Techniques for Unmanned Aerial Vehicles: Review and Challenges," Drones, vol. 7, no. 2, Jan. 2023, Art. no. 89. DOI: https://doi.org/10.3390/drones7020089
L. Xin, Z. Tang, W. Gai, and H. Liu, "Vision-Based Autonomous Landing for the UAV: A Review," Aerospace, vol. 9, no. 11, Oct. 2022, Art. no. 634. DOI: https://doi.org/10.3390/aerospace9110634
C. V. Angelino, V. R. Baraniello, and L. Cicala, "UAV Position and Attitude Estimation Using IMU, GNSS and Camera," in 15th International Conference on Information Fusion, Singapore, 2012, pp. 735–742.
L. Qi, T. Zhang, G. Chen, and W. Wang, "Robust Unmanned Aerial Vehicles Tracking Amid Electronic Interference Utilizing Auxiliary Particle Filtering," PLOS One, vol. 20, no. 9, Sept. 2025, Art. no. e0333009. DOI: https://doi.org/10.1371/journal.pone.0333009
K. Yue, "Multi-Sensor Data Fusion for Autonomous Flight of Unmanned Aerial Vehicles in Complex Flight Environments," Drone Systems and Applications, vol. 12, pp. 1–12, Jan. 2024. DOI: https://doi.org/10.1139/dsa-2024-0005
F. Bearzot et al., "Kinematics of an Alpine Rock Glacier from Multi-Temporal UAV Surveys and GNSS Data," Geomorphology, vol. 402, Apr. 2022, Art. no. 108116. DOI: https://doi.org/10.1016/j.geomorph.2022.108116
S.-M. Oh, "Multisensor Fusion for Autonomous UAV Navigation Based on the Unscented Kalman Filter with Sequential Measurement Updates," in 2010 IEEE Conference on Multisensor Fusion and Integration, Salt Lake City, UT, USA, Sept. 2010, pp. 217–222. DOI: https://doi.org/10.1109/MFI.2010.5604461
F. Han, "Modeling and Analysis of a Micromotor with an Electrostatically Levitated Rotor," Chinese Journal of Mechanical Engineering, vol. 22, no. 01, 2009, Art. no. 1. DOI: https://doi.org/10.3901/CJME.2009.01.001
J. P. Muñoz, M. E. Magaña, and E. Cotilla‐Sanchez, "Adaptive Master–Slave Unscented Kalman Filter for Grid Voltage Frequency Estimation," IET Signal Processing, vol. 12, no. 4, pp. 496–505, June 2018. DOI: https://doi.org/10.1049/iet-spr.2016.0199
J.-R. Chou, "An Integrative Review Exploring the Development of Sustainable Product Design in the Technological Context of Industry 4.0," Advanced Engineering Informatics, vol. 62, Oct. 2024, Art. no. 102689. DOI: https://doi.org/10.1016/j.aei.2024.102689
J. Strickland, Predictive Modeling and Analytics. Morrisville, NC, USA: Lulu, Inc., 2014.
J. Dong, X. Ren, S. Han, and S. Luo, "UAV Vision Aided INS/Odometer Integration for Land Vehicle Autonomous Navigation," IEEE Transactions on Vehicular Technology, vol. 71, no. 5, pp. 4825–4840, May 2022. DOI: https://doi.org/10.1109/TVT.2022.3151729
S. Boiteau, F. Vanegas, J. Sandino, F. Gonzalez, and J. Galvez-Serna, "Autonomous UAV Navigation for Target Detection in Visually Degraded and GPS Denied Environments," in 2023 IEEE Aerospace Conference, Big Sky, MT, USA, Mar. 2023, pp. 1–10. DOI: https://doi.org/10.1109/AERO55745.2023.10115544
G. Zhang and L.-T. Hsu, "Intelligent GNSS/INS Integrated Navigation System for a Commercial UAV Flight Control System," Aerospace Science and Technology, vol. 80, pp. 368–380, Sept. 2018. DOI: https://doi.org/10.1016/j.ast.2018.07.026
X. Xu, X. Tian, L. Zhou, and Y. Li, "A Decision-Tree Based Multiple-Model UKF for Attitude Estimation Using Low-Cost MEMS MARG Sensor Arrays," Measurement, vol. 135, pp. 355–367, Mar. 2019. DOI: https://doi.org/10.1016/j.measurement.2018.11.062
B. Wang, D. Liu, W. Wang, and X. Peng, "A Hybrid Approach for UAV Flight Data Estimation and Prediction Based on Flight Mode Recognition," Microelectronics Reliability, vol. 84, pp. 253–262, May 2018. DOI: https://doi.org/10.1016/j.microrel.2018.03.032
A. El-alami, Y. Nadir, and K. Mansouri, "An Efficient Geometric Transformation-Based Approach for Multi-UAV Image Stitching," Engineering, Technology & Applied Science Research, vol. 15, no. 4, pp. 25507–25513, Aug. 2025. DOI: https://doi.org/10.48084/etasr.11719
Y. Yin, Z. Wang, L. Zheng, Q. Su, and Y. Guo, "Autonomous UAV Navigation with Adaptive Control Based on Deep Reinforcement Learning," Electronics, vol. 13, no. 13, June 2024, Art. no. 2432. DOI: https://doi.org/10.3390/electronics13132432
A. Assad, W. Khalaf, and I. Chouaib, "Novel Adaptive Fuzzy Extended Kalman Filter for Attitude Estimation in GPS-Denied Environment," Gyroscopy and Navigation, vol. 10, no. 3, pp. 131–146, July 2019. DOI: https://doi.org/10.1134/S2075108719030027
X. Dai, V. Nateghi, H. Fourati, and C. Prieur, "Q-Learning-Based Noise Covariance Adaptation in Kalman Filter for MARG Sensors Attitude Estimation," in IEEE International Symposium on Inertial Sensors and Systems, Avignon, France, May 2022, pp. 1–6. DOI: https://doi.org/10.1109/INERTIAL53425.2022.9787752
Y. Pandey, R. Bhattacharyya, and Y. N. Singh, "Robust Attitude Estimation with Quaternion Left-Invariant EKF and Noise Covariance Tuning," in 2025 17th International Conference on Computer and Automation Engineering, Perth, Australia, Mar. 2025, pp. 562–567. DOI: https://doi.org/10.1109/ICCAE64891.2025.10980574
Á. Odry, R. Fullér, I. J. Rudas, and P. Odry, "Kalman Filter for Mobile-Robot Attitude Estimation: Novel Optimized and Adaptive Solutions," Mechanical Systems and Signal Processing, vol. 110, pp. 569–589, Sept. 2018. DOI: https://doi.org/10.1016/j.ymssp.2018.03.053
Á. Odry, I. Kecskes, P. Sarcevic, Z. Vizvari, A. Toth, and P. Odry, "A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots," Sensors, vol. 20, no. 3, Feb. 2020, Art. no. 803. DOI: https://doi.org/10.3390/s20030803
Z. Fan, Y. Lin, Y. Ai, and H. Xu, "Adaptive Task Migration Strategy with Delay Risk Control and Reinforcement Learning for Emergency Monitoring," Scientific Reports, vol. 14, no. 1, July 2024, Art. no. 17606. DOI: https://doi.org/10.1038/s41598-024-67886-x
C. Zhang, C. Xu, G. Li, and B. He, "A Distributed Task Allocation Approach for Multi-UAV Persistent Monitoring in Dynamic Environments," Scientific Reports, vol. 15, no. 1, Feb. 2025, Art. no. 6437. DOI: https://doi.org/10.1038/s41598-025-89787-3
Y. Xu, Y. Wei, D. Wang, K. Jiang, and H. Deng, "Multi-UAV Path Planning in GPS and Communication Denial Environment," Sensors, vol. 23, no. 6, Mar. 2023, Art. no. 2997. DOI: https://doi.org/10.3390/s23062997
R. Alharthi, "Enhancing Unmanned Aerial Vehicle and Smart Grid Communication Security Using a ConvLSTM Model for Intrusion Detection," Frontiers in Energy Research, vol. 12, Dec. 2024, Art. no. 1491332. DOI: https://doi.org/10.3389/fenrg.2024.1491332
I. López-Villegas, E. A. Martínez-Rios, J. Izquierdo-Reyes, R. Bustamante-Bello, and F. Falcone, "A Systematic Literature Review of Emergency Communications Assisted by Unmanned Aerial Vehicles," Ad Hoc Networks, vol. 182, Mar. 2026, Art. no. 104063. DOI: https://doi.org/10.1016/j.adhoc.2025.104063
Downloads
How to Cite
License
Copyright (c) 2026 Dmytro Borovyk, Mykola Zaretskyi
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
