Computational Intelligent Algorithms in Multi-Sensor Data Fusion for UAV Detection and Identification: Challenges and Opportunities
Abstract
Nowadays, counter-Unmanned Aerial Vehicle (c-UAV) applications include multisensory devices, such as electro-optical, thermal, acoustic, radar and radio frequency sensors, the data
of which can be combined to increase confidence in hazard identification. Object identification, classification, multi-object tracking, and multisensory information fusion are just a few of the complex challenges that occur as a result. In recent years, researchers have made significant progress using deep learning-based approaches to accomplish similar tasks for generic objects, but using deep learning for UAV detection and classification is a new idea. Consequently, there is a need to offer an overview of deep learning technologies applied to c-UAV related tasks using multisensor data. The significant increase in the number of articles on "c-UAV systems" in recent years shows that research in this area still has enormous opportunities. This paper aims to describe improvements in deep learning on c- UAV-related tasks when applied to data from multiple sensors and multisensor information fusion and make recommendations for using deep learning algorithms in UAV detection and identification.
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