Spatial Information-Aware Flight Safety Forecasting Model for Unmanned Aerial Vehicles Based on Deep Learning and Grey Analysis

Abstract

Ensuring flight safety for unmanned aerial vehicles (UAVs) is a critical concern, necessitating effective mathematical modeling for safety forecasting in both academic and industrial contexts. This study addresses this need by combining the capabilities of deep neural networks and grey analysis to create a comprehensive mathematical modeling approach focused on spatial information service (SIS). The paper introduces a novel spatial information-aware flight security forecasting model for UAVs, emphasizing the transformative impact of the new methodology. Traditionally, factors influencing flight safety are identified and formulated based on SIS chain technology, SIS management rules, SIS business processes, and spatial SIS chain verification. To address the challenges posed by significant data volatility and missing data in the sample dateset, a non-equally spaced GM (1,1) model with an approximate non-simultaneous exponential law series is developed for prediction. Subsequently, multiple influencing factors are encoded and input into a specific BP neural network structure. The paper concludes with simulation experiments to evaluate the proposed model. The results of the simulation analysis demonstrate that the integration of deep learning and grey analysis in the proposed model effectively recognizes flight security risks with high efficiency. This underscores the transformative potential of the new approach in enhancing UAV flight safety forecasting.

Keywords

• Deep learning
• aerospace safety
• spatial databases
• unmanned aerial vehicles