基于改进孤立森林算法的无人机异常行为检测

doi:10.7527/S1000-6893.2022.26789

电子电气工程与控制

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基于改进孤立森林算法的无人机异常行为检测

唐立^1,3, 郝鹏^1,3, 任沛阁², 张祖耀^1,4, 何翔^1,4, 张学军^1,4

1. 西华大学智能空地融合载具及管控教育部工程研究中心, 成都 610039;
2. 国家空域管理中心, 北京 100094;
3. 西华大学汽车与交通学院, 成都 610039;
4. 西华大学航空航天学院, 成都 610039

收稿日期:2021-12-09 修回日期:2022-03-23 出版日期:2022-08-15 发布日期:2022-03-22
通讯作者: 唐立,E-mail:tangli@mail.xhu.edu.cn E-mail:tangli@mail.xhu.edu.cn
基金资助:
国家自然科学基金(5210120621);横向科研基金(RH1900010872)

UAV abnormal behavior detection based on improved iForest algorithm

TANG Li^1,3, HAO Peng^1,3, REN Peige², ZHANG Zuyao^1,4, HE Xiang^1,4, ZHANG Xuejun^1,4

1. Intelligent Air-Ground Fusion Vehicle and Control Engineering Research Center of the Ministry of Education, Xihua University, Chengdu 610039, China;
2. National Airspace Management Center, Beijing 100094, China;
3. School of Automobile and Transportation, Xihua University, Chengdu 610039, China;
4. School of Aeronautics and Astronautics, Xihua University, Chengdu 610039, China

Received:2021-12-09 Revised:2022-03-23 Online:2022-08-15 Published:2022-03-22
Supported by:
National Natural Science Foundation of China (5210120621);Horizontal Scientific Research Fund (RH1900010872)

摘要/Abstract

摘要： 为进一步提高低空无人机的管控能力,突破对合作型无人机运行状态的精准监控和非合作型无人机任务类型快速研判的关键技术,对无人机异常行为的检测方法进行研究。首先,定义了合作型和非合作型无人机运行过程中的异常行为,并对两类无人机的运行参数进行分析,明确了各种运行特征及其判定参数的提取方法。随后,提出了基于Sobel Operator-CNN算法的无人机类型判定方法。最后,改进孤立森林算法,提出动态最大生长高度的方法,对合作型和非合作型无人机的异常行为进行判别,根据数据节点判定无人机任务特点以及异常类型。基于ardupilot-airsim仿真平台的测试结果表明,改进的孤立森林算法具有收敛速度快、准确度高的特点,对异常行为的识别精准率达到96.4%,超过传统算法3.2%。

关键词: 无人机监控技术, 无人机识别分类, 异常行为检测, Sobel Operator-CNN算法, 改进孤立森林(iForest)算法

Abstract: To further improve the control ability of low altitude UAV and break through the key technologies of accurate monitoring of cooperative UAV operation state and rapid judgment of the type of non-cooperative UAV task, a method for UAV abnormal behavior detection is studied. Firstly, the abnormal behaviors in the operation of cooperative and non-cooperative UAVs are defined. The operation parameters of the two types of UAVs are analyzed, and the extraction methods of various operation characteristics and judgment parameters are determined. Then, a UAV type determination method is proposed based on the Sobel Operator-CNN algorithm. Finally, the improved isolated Forest (iForest) algorithm based on the method of dynamic maximum growth height is proposed to distinguish the abnormal behavior of cooperative and non-cooperative UAVs. The task characteristics and abnormal types of UAVs are determined according to data nodes. The results of test based on the ardupilot-airsim experimental platform show that the improved iForest algorithm has the characteristics of fast convergence speed and high accuracy, and abnormal-behavior recognition accuracy of the algorithm is 96.4%, which is 3.2% higher than that of the traditional algorithm.

Key words: UAV monitoring technology, UAV identification and classification, abnormal behavior detection, Sobel Operator-CNN algorithm, improved isolated Forest (iForest) algorithm

中图分类号:

V279⁺.2

唐立, 郝鹏, 任沛阁, 张祖耀, 何翔, 张学军. 基于改进孤立森林算法的无人机异常行为检测[J]. 航空学报, 2022, 43(8): 326789-326789.

TANG Li, HAO Peng, REN Peige, ZHANG Zuyao, HE Xiang, ZHANG Xuejun. UAV abnormal behavior detection based on improved iForest algorithm[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(8): 326789-326789.

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主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

基于改进孤立森林算法的无人机异常行为检测

UAV abnormal behavior detection based on improved iForest algorithm

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