Review of RF-based drone classification: Techniques, datasets, and challenges

Abstract

Introduction/purpose: This article analyzes the publicly available literature on drone classification in the radio frequency domain, focusing on detection and identification. Drones are increasingly used for illegal purposes, making classification techniques crucial. This review paper covers passive radio frequency sensors, classification techniques, and datasets that highlight the challenges. Methods: Researchers are developing antidrone solutions because drones have become valuable tools and targets for illegal activities. Due to the scope of the subject matter, the review included only the classification of drones via passive radio frequency sensors with a description of the classification techniques (set of algorithms, methods, and procedures) and the datasets used for performance testing. This study introduces a new categorization and offers deeper insights into publicly available drone classification techniques. Results: Based on the results of this study, it is apparent that deep learning algorithms are presently the most effective approach to addressing the challenge of drone classification within the radio frequency domain. One of the primary obstacles is the absence of a comprehensive standard for classifying drones in the radio frequency domain, which should be based on end-user requirements. Additionally, the results of two ablative experiments highlight the preprocessing of raw I/Q radio signals as an essential step in drone classification. Conclusion: In summary, the proposed categorization provides a valuable tool for literature review. Deep learning is the most effective technique for drone classification, but publicly available datasets with drone radio signals are limited. The key strength of this study is that it represents the first review of publicly available datasets with drone radio signals.