Research on signal noise reduction method of quad-rotor UAV

Zheng Xu

doi:10.1117/12.3027183

1 April 2024 Research on signal noise reduction method of quad-rotor UAV

Zheng Xu

Proceedings Volume 13077, Fourth International Conference on Signal Processing and Machine Learning (CONF-SPML 2024); 130770P (2024) https://doi.org/10.1117/12.3027183
Event: 4th International Conference on Signal Processing and Machine Learning (CONF-SPML 2024), 2024, Chicago, IL, United States

Abstract

The use of drones can be seen everywhere in life, and they are used in a wide range of ways. Collecting information and collecting images are also common uses. However, when drones are working, they are often affected by many factors, which ultimately leads to poor quality of data collected. Therefore, in order to increase drone efficiency and make the information obtained more accurate and reliable, many researchers choose to reduce signal noise on drones. Reduce or eliminate noise interference or clutter in drone signals through various technologies (such as deep learning, convolutional neural networks, etc.), thereby improving communication quality and system performance. Therefore, this article will analyze the currently used noise reduction methods, explain the advantages and disadvantages of each in use, and propose directions that can be changed based on the existing problems. By adding different algorithms, each neural network can be combined with a more complete algorithm, and how to choose the appropriate neural network in different environments to avoid shortcomings and maximize the efficiency of the drone. This article has practical significance for enhancing the performance and application of UAVs, and also provides direction and theoretical basis for subsequent research.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zheng Xu "Research on signal noise reduction method of quad-rotor UAV", Proc. SPIE 13077, Fourth International Conference on Signal Processing and Machine Learning (CONF-SPML 2024), 130770P (1 April 2024); https://doi.org/10.1117/12.3027183

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