Application of the Kalman Filter in Low-Altitude UAV 
Tracking

Kai Cui; Jianwei Zhao; Fang He; Nan Jiang; Weimin Jia

doi:10.70711/frim.v3i1.5897

Application of the Kalman Filter in Low-Altitude UAV Tracking

Kai Cui, Jianwei Zhao, Fang He, Nan Jiang, Weimin Jia

Abstract

With the rapid development of unmanned aerial vehicle (UAV) technology, low-altitude UAVs have been increasingly applied in
fields such as aerial photography, agricultural monitoring, and logistics distribution. However, low-altitude environments are complex and
variable, and UAVs are susceptible to various factors, such as air currents and obstacles during flight, leading to unstable flight trajectories
and increased tracking difficulty. Kalman Filter, as an effective state estimation algorithm, can fuse data from multiple sensors to accurately
estimate the position and velocity of UAVs, thereby improving the accuracy and robustness of low-altitude UAV tracking. This study reviews
the basic principles of the Kalman Filter and its application in low-altitude UAV tracking, analyzes the limitations of traditional Kalman Filter
in dealing with nonlinear systems, and discusses the application prospects of improved algorithms such as the Extended Kalman Filter (EKF)
and Unscented Kalman Filter (UKF).

Keywords

Kalman Filter; Low-altitude UAV; Tracking; State estimation

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References

[1] Y. Xia, L. Tian, A Review of Key Technologies for Air to Air Network Networking for Low-altitude Economy, Journal of Chongqing

University of Posts and Telecommunications (Natural Science Edition), 2024, 36 (04): 619-632.

[2] ZTE, White Paper on Integrated Sensing Low-altitude Network, Shanghai: ZTE Corporation, 2024.

[3] D. Q. Tang, X. Y. Liu, W. D. Deng, et al, Small unmanned aerial vehicle target localization method based on iterative unscented Kalman Filter, Command and Control and Simulation, 2019, 41 (1): 104-108.

[4] Y. P. Ren Research on Target Detection and Tracking Technology in Anti UAV Systems, Xi'an University of Technology, 2019.

[5] W. S. Chen, J. Liu, X. L. Chen, et al, Low-altitude non cooperative unmanned aerial vehicle target recognition based on motion model, 2019.

[6] S. Gao, J. Zhang, C. Wang, et al, Research on unmanned aerial vehicle target tracking based on kernel correlation filter and Kalman

Filter, International Conference on Electronic Communication and Artificial Intelligence (ICECAI). IEEE, 2023: 311-314.

[7] Y. L. Zhang, Research on Low Slow Small Target Space Localization and Tracking Technology, Xi'an University of Technology, 2024.

[8] S. Wang, Then A, Herschel R, UAV tracking based on unscented Kalman Filter for sense and avoid applications, International Radar

Symposium (IRS). IEEE, 2020: 250-255.

DOI: http://dx.doi.org/10.70711/frim.v3i1.5897

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