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随着无人机(Unmanned Aerial Vehicle, UAV)集成化与智能化的不断发展，UAV在军事领域和民用领域得到广泛应用。因此，对UAV的安全飞行提出更高的要求，而UAV异常检测在保障安全飞行、减少经济损失等方面有着重要作用。近年来，数据驱动的方法在特征提取、非线性问题求解和准确率等方面的优势，使其成为UAV异常检测的主流算法。对UAV异常类型及异常数据特点进行分析与总结。梳理并总结国内外UAV数据驱动的异常检测算法的研究现状，从监督学习、半监督学习和无监督学习三方面对UAV异常检测进行了归纳与总结，并分析了各类算法的优缺点。针对现有算法的研究现状，展望了未来UAV异常检测领域的发展趋势，旨在为后续相关研究提供参考。
Abstract：With the continuous development of Unmanned Aerial Vehicle(UAV) integration and intelligence, UAV are widely used in military and civilian fields. Therefore, higher requirements are placed on the safe flight of UAV, and UAV anomaly detection has an important role in ensuring safe flight and reducing economic losses. In recent years, the advantages of data-driven methods in feature extraction, nonlinear problem solving, and accuracy have made them the mainstream algorithms for UAV anomaly detection. The types of UAV anomalies and the characteristics of the anomaly data are analyzed and summarized. Current research status of UAV data-driven anomaly detection algorithms at home and abroad is summarized. The three aspects of UAV anomaly detection from supervised learning, semi-supervised learning and unsupervised learning are summarized and concluded, and the advantages and disadvantages of various algorithms are analyzed. The development trend of future UAV anomaly detection is prospected in view of current research status of existing algorithms, aiming to provide reference for the subsequent related research.
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基本信息:
中图分类号:V35;TP18
引用信息:
[1]王岩,李少波,张仪宗,等.数据驱动的无人机异常检测算法综述[J].无线电工程,2024,54(06):1407-1420.
2023-12-13
2023-12-13
2023-12-13