Study on Algorithm for Flight Conflict Detection Based on Deep Gaussian Process

In order to build temporal features of flight trajectory accurately, the Gaussian Process (GP) is applied to predict the future flight trajectory. Meanwhile, considering the non-linearity characteristics of the aircraft during the high maneuverability motion, the GP is combined with the deep belief network to formulate the deep GP which is applied to flight trajectory prediction. Based on the predicted trajectory, the probabilistic flight conflict detection based on deep GP is proposed and implemented in this paper. The Monte Carlo simulation and Markov Chain Monte Carlo sampling are proposed to compute the conflict probability for the proposed conflict detection method. Deep GP based flight trajectory approach can not only predict the nominal trajectory for aircraft, but also estimate the probabilistic distribution of the confidence interval for the predicted positions, which lays as solid data foundation for the conflict detection task. Experimental results on real data show that the proposed deep GP based trajectory prediction model can obtain higher accuracy and stability than that of baselines. In addition, by applying the predicted trajectory to the conflict detection algorithm, we can achieve the task with lower false alarm and longer warning time.