Journal Article

The Operational Safety Evaluation of UAVs Based on Improved Support Vector Machines

Liu, Shuguang, Zhou, Yulin

2025

Overview

In response to the challenge of dynamic adaptability in operational safety assessment for UAVs operating in complex operational environments, this study proposes a novel operational safety assessment method based on an Improved Support Vector Machine. An operational safety assessment index system encompassing four dimensions—operator, UAV platform, flight environment, flight mission—is constructed to provide a comprehensive foundation for evaluation. The method introduces a dynamic weighted information entropy mechanism based on a sliding window, overcoming the static features and delayed response of traditional SVM methods. Additionally, it integrates Gaussian and polynomial kernel functions to significantly enhance the generalization capability and classification accuracy of the SVM model in complex operational environments. Experimental results show that the proposed model demonstrates superior performance on test samples, effectively improving the accuracy of operational safety assessment for the Reconnaissance–Strike UAV in complex operational environments, and offering a novel methodology for UAV safety assessment.

• Publisher: MDPI AG
• Subject: Accident prevention / Adaptability / Algorithms / Aviation / Deception / Decision making / Entropy (Information theory) / Flight operations / Flying-machines / Kernel functions / Military aspects / Neural networks / operational safety assessment / Polynomials / Reconnaissance aircraft / Safety / Safety analysis / Support vector machines / SVM / UAV