Research On 4D Track-based Conflict Detection And Relief Method For Complex Routes

With the development of the civil aviation industry,the airspace is becoming more and more congested.When aircraft are in congested airspace,the problem of potential conflict between aircraft often occurs.How to efficiently plan the escape path also has important practical significance.According to statistics,in 2020,a total of 3.5206 million flights were performed by national passenger transport companies,of which 3.1164 million were regular flights,and the average flight regularity rate was 88.52%.In addition to airlines,air traffic control reasons and weather reasons,the factors that affect the flight punctuality rate are mainly caused by the multi-aircraft conflict caused by the route structure.Therefore,more efficient and accurate detection of conflicts between aircraft and timely conflict resolution has become the main means of mitigating conflicts.In this thesis,by optimizing the traditional randomization algorithm,based on the fourdimensional trajectory prediction,the randomly selected samples are controlled within the minimum prediction distance of the aircraft to narrow its scope and make it more credible,so as to accurately and efficiently simulate flight conflicts.Then,the improved sorting ant colony algorithm is used to plan the conflict flight trajectory to solve the conflict for the above conflict flights,to ensure the efficiency,safety and economy of the aircraft under conflict operation to the greatest extent,and to reduce the flight delay distance.The main research process of this thesis is firstly by sorting out the literature,briefly introducing the current research status of aircraft conflict detection and conflict resolution path planning at home and abroad,as well as the correlation between four-dimensional trajectory and conflict detection and path planning.Next,by using the track prediction method based on the equiangular route,the time and distance to reach the minimum separation distance of the aircraft in the airspace are predicted.By reaching the minimum interval time,the randomization algorithm is optimized,and the conflict probability is calculated in the range of two minutes before and after the time.This can not only improve the timeliness of the randomization algorithm,but also ensure the accuracy of the randomization algorithm.Then,through simulation,the results of Monte Carlo,the randomization algorithm and the improved randomization algorithm are compared.Then the ant colony algorithm is used to get rid of the conflict path.The improved algorithm is to join the sorting-based ant colony system,and to get rid of the conflict path through experimental simulation,and then compare with the results of traditional ant colony and the introduction of elite ant colony strategy algorithm Prove the superiority of the algorithm.