Research On Cooperative Deployment Of UAV Communication And Localization In Emergency Satellite Denial Scenario

In the aftermath of a disaster,stable communication between rescuers and between those in the disaster area and external commanders is required to facilitate rescue efforts.In addition,the command center needs real-time positioning and navigation services to clarify the location of rescuers and trapped people in the disaster area to implement precise command.However,ground communication equipment is often severely damaged after a disaster,the complex geological conditions of the disaster area make it difficult for emergency communication vehicles to enter,and the complex propagation environment in mountainous forest areas causes satellite signal rejection,making it difficult to continue communication and positioning business capabilities.UAVs can meet the post-disaster emergency communication and positioning needs for efficiency and reliability due to their excellent adaptability,rapid and flexible deployment,and convenient data transmission.However,due to the complex terrain and harsh environment in the affected areas,and the limited energy available for UAVs,the following three challenges still exist in establishing an emergency communication system using UAVs after a disaster:first,due to the complex environment in mountainous forest areas resulting in satellite signal rejection,the a priori location and distribution of users are difficult to obtain in advance,resulting in poor UAV communication coverage;second,the uneven distribution and high mobility of ground users in the disaster area Finally,there are a large number of non-visual links around the trapped users due to signal blocking caused by trees and buildings,resulting in poor performance of UAV emergency content distribution and positioning.In order to solve the above problems,this thesis studies the UAV pass-guide cooperative deployment method in emergency rescue satellite denial scenarios as follows:First,in the post-disaster emergency satellite denial scenario,a twostage UAV through-guidance network deployment scheme is proposed for the problems of missing a priori location information of ground users and limited UAV energy.Specifically,this thesis implements the overall deployment scheme by sequentially solving two sub-problems:areaoriented positioning deployment and joint communication and positioning deployment.In the first stage,the UAV is deployed to improve the average positioning performance of the area and to initially locate the ground users,and then,based on the ground user distribution obtained after deployment,the UAV position is adjusted in the second stage so that each user is covered and meets a certain positioning accuracy.The residual energy of each UAV is also taken into account to maximize the serviceable time of the network after deployment.In this thesis,an enhanced genetic algorithm is proposed to improve on the shortcomings of traditional genetic algorithms such as complex coding and decoding,slow convergence speed and easy to fall into local optimum.In addition,when UAVs perform communication coverage,for the problem of poor coverage caused by user location errors,the possible real locations of users are simulated by discrete points to improve the actual communication coverage.The simulation results show that the proposed through-guide network deployment scheme improves the regional positioning performance and the service time of the network after UAV deployment.Secondly,in the post-disaster emergency rescue scenario,a UAV positioning network adaptive adjustment scheme is proposed for the joint optimization of UAV positioning task assignment and movement paths in response to the poor positioning performance of the static network for ground mobile personnel.Specifically,firstly,a Boolean vector is defined as an indication vector for users to indicate the selected UAV nodes,and the current optimal set of UAVs is assigned to each user by adjusting this indication vector.At the same time,considering the high mobility of the ground personnel,it is also necessary to continuously adjust the position of the UAVs so as to maintain a better geometric configuration with the users.In order to improve the user’s positioning accuracy while minimizing the UAV’s movement path length,this thesis jointly optimizes the positioning task assignment and UAV position within each time slot.In addition,a weighted artificial bee colony algorithm with dynamic update of selection probability is proposed in this thesis to improve the performance of solving the proposed joint optimization problem.Simulation results show that the proposed scheme can improve the average positioning accuracy of ground mobile users and reduce the path length of UAVs flying in the full time slot compared with existing schemes in the case of ground users with high mobility.Finally,in the post-disaster emergency content distribution scenario,for the problem of poor communication and positioning performance due to the existence of non-visual links in the environment,this thesis proposes a greedy algorithm-based iterative deployment scheme for UAV passthrough networks while avoiding non-visual links and optimizing the UAV position and antenna orientation.Specifically,a mathematical model for establishing line-of-sight link communication conditions between UAVs and users is firstly given based on environmental a priori information.Then the incremental communication and positioning performance for the network with each added UAV is derived.An iterative UAV deployment scheme is proposed,in which one UAV is added in each iteration,and the position and antenna orientation of that UAV are jointly optimized by an improved particle swarm algorithm to maximize the increment of communication rate and positioning accuracy for ground users until the performance increment is less than a set threshold or the number of UAVs reaches an upper limit.Simulation results show that the proposed scheme can improve the communication rate and average positioning accuracy of ground users with a limited number of UAVs.