Research On The UAV Conflict Avoidance Method And Hedging Equipment Based On ADS-B

The UAV industry has made a great development,because it is widely used in civil and military fields.With the increase in the number of UAV applications,the low-altitude airspace becomes more and more crowded,and the safety of UAVs during missions is increasingly threatened.The UAV’s security performance,especially its ability to prevent collisions,is an important factor affecting the development of UAVs.The UAV conflict detection and avoidance technology is an important guarantee against the collision accidents of UAVs.ADS-B has the advantages of comprehensiveness,quickness,and high precision.It can interact with ground operators.With the advent of ADS-B,the conflict avoidance achieves a leap from person to nobody.The hedging problem of UAV is divided into conflict detection and conflict avoidance.Based on ADS-B technology,the paper studies the hedging problem of UAV.Firstly,the ADS-B technology is comprehensively analyzed and compared with secondary surveillance radar.The superiority of the ADS-B system in the hedging problem of the UAV is summed up.In combination with the minimum interval of UAV,a conflict detection model based on ADS-B and UAV performance is established,and the conflict perception process and conflict resolution model are introduced in detail.Secondly,aiming at the problem of conflict avoidance and minimized its cost,an improved ant colony algorithm is proposed.According to the natural selection rules,adaptive adjustment strategy is adopted to balance the relative importance of trajectory and visibility at different stages of the algorithm.The disturbance factor is introduced to rationally allocate the transition probability to various stages of optimization to prevent the algorithm from becoming premature convergence.Through simulation and verification,the proposed algorithm has a higher convergence accuracy,and successfully resolved the conflict of two UAVs.Then,aiming at the problem that the ant colony algorithm convergence rate cannot meet the needs when the scale of the optimization object is enlarged,using the characteristics of easy expansion of ant colony algorithm,a new hybrid genetic-ant colony algorithm is proposed by analyzing the characteristics of other algorithms,using the feature that the initial convergence rate of the genetic algorithm is fast to form a better solution space,and then the ant colony algorithm is used to further optimize the solutionspace.The cohesion condition of the fusion algorithm is designed to switch algorithm in time through the state monitor to avoid redundant iterations.In the genetic phase,the concept of chromosome similarity is introduced to reasonably arrange the probability of crossover and mutation to improve the solution space formed by genetic algorithms.The simulation results show that the fusion algorithm has a great improvement in convergence speed and accuracy.Taking four UAVs as examples,successfully achieved the avoidance of multi-UAV conflict,which verifies the effectiveness of the improved strategy.As a general optimization algorithm,the algorithm can also be applied to target identification,path planning and other issues,which has important research significance and wide application value.Finally,a conflict avoidance equipment for UAV based on ADS-B system is built,including a mobile ground command platform and airborne conflict avoidance equipment.The ADS-B simulator and ADS-B RF pulse parameter detector are used to verify the equipment.The airborne conflict avoidance equipment is added to the UAV to conduct a flight test.The test results verify the effectiveness of the work done by the paper.