Predictive Modeling of Environmental Impact on Drone Datalink Communication System.

In this study, we introduce an innovative model for evaluating the impact of environmental factors on drone-to-ground control station datalink communications. Our approach integrates both deterministic and stochastic processes to account for small-scale and large-scale fading effects, encompassing propagation attenuation, the Rician fading model, and Gaussian noise to accurately reflect real-world conditions. The model is implemented on signals transmitted using spread spectrum modulation. Through a comparative analysis of the model's predictions against actual signals received in three distinct environments, the model's efficacy in diverse scenarios is affirmed. Error metrics obtained from Monte Carlo simulations are employed to validate the theoretical results against experimental data. The proposed approach is pivotal for predicting the transmission range and understanding the electromagnetic susceptibility of the datalink, offering a substantial contribution to the optimization of remote drone control. [ABSTRACT FROM AUTHOR]