Survey on anomaly detection algorithms for unmanned aerial vehicle flight data

doi:10.11772/j.issn.1001-9081.2022060808

Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (6): 1833-1841.DOI: 10.11772/j.issn.1001-9081.2022060808

Special Issue: 数据科学与技术；综述

• Data science and technology • Previous Articles Next Articles

Survey on anomaly detection algorithms for unmanned aerial vehicle flight data

Chaoshuai QI¹, Wensi HE², Yi JIAO³, Yinghong MA¹(), Wei CAI⁴, Suping REN⁴

^1.School of Telecommunications Engineering，Xidian University，Xi’an Shaanxi 710071，China
^2.Institute 706，The Second Academy of China Aerospace Science and Industry Corporation，Beijing 100854，China
^3.School of Telecommunications and Information Engineering，Xi’an University of Posts and Telecommunications，Xi’an Shaanxi 710121，China
^4.Center of Innovation and Application，China Academy of Aerospace Aerodynamics，Beijing 100074，China

Received:2022-06-06 Revised:2022-11-09 Accepted:2022-11-09 Online:2023-06-08 Published:2023-06-10
Contact: Yinghong MA
About author:QI Chaoshuai， born in 1998， M. S. candidate. His research interests include machine learning， data mining.
HE Wensi， born in 1987， M. S.， engineer. Her research interests include artificial intelligence， machine learning.
JIAO Yi， born in 1980， Ph. D.， lecturer. His research interests include edge computing， network resource management.
CAI Wei， born in 1985， M. S.， senior engineer. His research interests include unmanned aerial vehicle measurement and control， payload application.
REN Suping， born in 1993， M. S. Her research interests include unmanned aerial vehicle overall design， electrical system design， unmanned aerial vehicle modeling and simulation.
Supported by:
National Defence Basic Scientific Research Program in “the 14th Five-Year Plan”(JCKY2020203XXXX);Fundamental Research Funds for Central Universities(JB210106)

无人机飞行数据异常检测算法综述

祁超帅¹, 何文思², 焦毅³, 马英红¹(), 蔡伟⁴, 任素萍⁴

^1.西安电子科技大学通信工程学院, 西安 710071
^2.中国航天科工集团第二研究院七〇六所, 北京 100854
^3.西安邮电大学通信与信息工程学院, 西安 710121
^4.中国航天空气动力技术研究院创新与应用中心, 北京 100074

通讯作者: 马英红
作者简介:祁超帅（1998—），男，河南开封人，硕士研究生，主要研究方向：机器学习、数据挖掘
何文思（1987—），女，北京人，工程师，硕士，CCF会员，主要研究方向：人工智能、机器学习
焦毅（1980—），男，辽宁沈阳人，讲师，博士，主要研究方向：边缘计算、网络资源管理
马英红（1981—），女，河北衡水人，副教授，博士，主要研究方向：人工智能、边缘计算、云网融合、无线组网Email：yhma@xidian.edu.cn
蔡伟（1985—），男，江苏盐城人，高级工程师，硕士，主要研究方向：无人机测控、载荷应用
任素萍（1993—），女，河北唐山人，硕士，主要研究方向：无人机总体设计、电气系统设计、无人机建模仿真。
基金资助:
“十四五”国防基础科研计划项目(JCKY2020203XXXX);中央高校基本科研业务费专项(JB210106)

Abstract

Abstract:

Focused on the issue of anomaly detection for Unmanned Aerial Vehicle （UAV） flight data in the field of UAV airborne health monitoring， firstly， the characteristics of UAV flight data， the common flight data anomaly types and the corresponding demands on anomaly detection algorithms for UAV flight data were presented. Then， the existing research on UAV flight data anomaly detection algorithms was reviewed， and these algorithms were classified into three categories： prior-knowledge based algorithms for qualitative anomaly detection， model-based algorithms for quantitative anomaly detection， and data-driven anomaly detection algorithms. At the same time， the application scenarios， advantages and disadvantages of the above algorithms were analyzed. Finally， the current problems and challenges of UAV anomaly detection algorithms were summarized， and key development directions of the field of UAV anomaly detection were prospected， thereby providing reference ideas for future research.

Key words: Unmanned Aerial Vehicle (UAV), airborne health monitoring, flight data, data mining, anomaly detection

摘要：

针对无人机（UAV）机载健康状态监测领域的UAV飞行数据异常检测问题，首先阐述了UAV飞行数据的特点、常见的飞行数据异常类型及对异常检测算法的要求；然后梳理了UAV飞行数据异常检测算法的研究现状，并归为3大类：基于先验知识的定性异常检测算法、基于模型的定量异常检测算法和基于数据驱动的异常检测算法，同时分析了各类算法的应用场景和优缺点；最后总结了UAV飞行数据异常检测算法目前存在的问题和挑战，展望了未来UAV飞行数据异常检测领域的重点发展方向，为新的研究提供了参考思路。

关键词: 无人机, 机载健康监测, 飞行数据, 数据挖掘, 异常检测

CLC Number:

TP277

Chaoshuai QI, Wensi HE, Yi JIAO, Yinghong MA, Wei CAI, Suping REN. Survey on anomaly detection algorithms for unmanned aerial vehicle flight data[J]. Journal of Computer Applications, 2023, 43(6): 1833-1841.

祁超帅, 何文思, 焦毅, 马英红, 蔡伟, 任素萍. 无人机飞行数据异常检测算法综述[J]. 《计算机应用》唯一官方网站, 2023, 43(6): 1833-1841.

Figures/Tables 14

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类型	优点	缺点
基于先验知识的定性算法	无需严格的数据定量分析，实现简单	先验知识少，异常种类难以完全把握
基于模型的定量算法	模型准确度较高	模型建立困难，移植性差
基于数据驱动的算法	通用性强，先验知识需求小	大多数算法复杂度高，依赖数据的质量

类型	优点	缺点
基于先验知识的定性算法	无需严格的数据定量分析，实现简单	先验知识少，异常种类难以完全把握
基于模型的定量算法	模型准确度较高	模型建立困难，移植性差
基于数据驱动的算法	通用性强，先验知识需求小	大多数算法复杂度高，依赖数据的质量

算法类型	基本思想	优点	缺点
基于统计	异常点分布在低概率区域	数据处理速度快，能有效识别单维数据的离群点	很难确定概率分布假设，不适用于高维数据
基于分类	训练决策边界分离异常数据	适用于高维且标签明确的场景	过于依赖数据标签
基于相似性	根据数据间的相似度识别异常数据	检测准确度高	大多相似性算法计算复杂度高
基于预测	通过数据与预测值的残差判断异常	可较好地拟合数据的趋势性	对噪声数据敏感

算法类型	基本思想	优点	缺点
基于统计	异常点分布在低概率区域	数据处理速度快，能有效识别单维数据的离群点	很难确定概率分布假设，不适用于高维数据
基于分类	训练决策边界分离异常数据	适用于高维且标签明确的场景	过于依赖数据标签
基于相似性	根据数据间的相似度识别异常数据	检测准确度高	大多相似性算法计算复杂度高
基于预测	通过数据与预测值的残差判断异常	可较好地拟合数据的趋势性	对噪声数据敏感

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Survey on anomaly detection algorithms for unmanned aerial vehicle flight data

无人机飞行数据异常检测算法综述

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