Anomaly Detection of UAV State Data Based on Single-Class Triangular Global Alignment Kernel Extreme Learning Machine  

作　　者：Feisha Hu Qi Wang Haijian Shao Shang Gao Hualong Yu 

机构地区：[1]School of Computer,Jiangsu University of Science and Technology,Zhenjiang,212100,China [2]Department of Electrical and Computer Engineering,University of Nevada,Las Vegas,NV,89154,USA

出　　处：《Computer Modeling in Engineering & Sciences》2023年第9期2405-2424,共20页工程与科学中的计算机建模（英文）

基　　金：supported by the Natural Science Foundation of The Jiangsu Higher Education Institutions of China(Grant No.19JKB520031).

摘　　要：Unmanned Aerial Vehicles(UAVs)are widely used and meet many demands in military and civilian fields.With the continuous enrichment and extensive expansion of application scenarios,the safety of UAVs is constantly being challenged.To address this challenge,we propose algorithms to detect anomalous data collected from drones to improve drone safety.We deployed a one-class kernel extreme learning machine(OCKELM)to detect anomalies in drone data.By default,OCKELM uses the radial basis(RBF)kernel function as the kernel function of themodel.To improve the performance ofOCKELM,we choose a TriangularGlobalAlignmentKernel(TGAK)instead of anRBF Kernel and introduce the Fast Independent Component Analysis(FastICA)algorithm to reconstruct UAV data.Based on the above improvements,we create a novel anomaly detection strategy FastICA-TGAK-OCELM.The method is finally validated on the UCI dataset and detected on the Aeronautical Laboratory Failures and Anomalies(ALFA)dataset.The experimental results show that compared with other methods,the accuracy of this method is improved by more than 30%,and point anomalies are effectively detected.

关 键 词：UAV safety kernel extreme learning machine triangular global alignment kernel fast independent component analysis 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]