Flight Safety Assessment via Weighted Radar Chart Optimization

To address the issues of fixed weights and slow performance in traditional safety assessments for cooperative flights between unmanned aerial vehicle and manned aircraft, this paper suggests a new model that optimizes weights using grey correlation analysis-particle swarm optimization (GRA-PSO) and creates an enhanced radar chart that includes changes over time and space. By adjusting the combined weights to different angles in a non-uniform coordinate system, the visual representation of multiple indicators is updated in real-time. By mapping the combined weights to non-uniform coordinate system angles, the multidimensional indicator visualization is dynamically updated. Simulation experiments show that the system can capture flight state anomalies in real time and reduce the average delay.