Intensive Review of Drones Detection and Tracking: Linear Kalman Filter Versus Nonlinear Regression, an Analysis Case-Reference-Cited by-同舟云学术

Intensive Review of Drones Detection and Tracking: Linear Kalman Filter Versus Nonlinear Regression, an Analysis Case

Published:2023-02-06 Issue:5 Volume:30 Page:2811-2830
ISSN:1134-3060
Container-title:Archives of Computational Methods in Engineering
language:en
Short-container-title:Arch Computat Methods Eng

Author:

Zitar Raed Abu^ORCID,Mohsen Amani,Seghrouchni Amal ElFallah,Barbaresco Frederic,Al-Dmour Nidal A.

Funder

Thales Group

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computer Science Applications

Link

https://link.springer.com/content/pdf/10.1007/s11831-023-09894-0.pdf

Reference93 articles.

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3. Ahuja K, Jiang Y, Goel M, Harrison C (2021) Vid2doppler: synthesizing doppler radar data from videos for training privacy-preserving activity recognition. In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pp 1–10

4. Akyon FC, Eryuksel O, Ozfuttu KA, Altinuc SO (2021) Track boosting and synthetic data aided drone detection. In: 2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp 1–5. IEEE

5. Bochkovskiy A, Wang C, Liao HM (2020) Yolov4: optimal speed and accuracy of object detection. CoRR, abs/2004.10934. https://arxiv.org/abs/2004.10934. arXiv:2004.10934

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