基于单类三角全局对齐核极端学习机的UAV状态数据异常检测
Anomaly Detection of UAV State Data Based on Single-class Triangular Global Alignment Kernel Extreme Learning Machine
摘要：
无人机（Unmanned Aerial Vehicles，UAV）在军事和民用领域有着广泛的应用和需求。随着应用场景的不断丰富和广泛扩展，无人机的安全性不断受到挑战。为了应对这一挑战，我们提出了检测从无人机收集的异常数据的算法，以提高无人机的安全性。我们部署了一个单类内核极端学习机（OCKELM）来检测无人机数据中的异常。默认情况下，OCKELM使用径向基（RBF）核函数作为模型的核函数。为了提高OCKELM的性能，我们选择了一个三角形的全局对齐核（TGAK）代替RBF核，并引入快速独立分量分析（FastICA）算法来重建无人机数据。在此基础上，提出了一种新的异常检测策略FastICA-TGAK-OCELM。该方法最终在UCI数据集上进行了验证，并在航空实验室故障和异常（ALFA）数据集上进行了检测。实验结果表明，与其他方法相比，该方法的精度提高了30%以上，有效地检测出了点异常。
英文摘要：
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 the model. To improve the performance of OCKELM, we choose a Triangular Global Alignment Kernel (TGAK) instead of an RBF 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.
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