Research On Cluster Communication And Weapon Resource Allocation Technology For UAV Swarms

Unmanned Aerial Vehicles(UAVs)can carry reconnaissance and jamming equipment while flying flexibly.They have efficient data collection and flexible deployment capabilities when performing tasks such as target search and electronic countermeasures,making them widely used in military fields.Compared to the limited energy and communication load capacity of a single UAV,UAV swarms have better flexibility,robustness,and higher task execution efficiency.As the number of UAVs in the swarm increases,the communication channels will be congested and collisions will occur frequently,especially when spectrum resources are scarce in the environment.Frequent congestion and collisions significantly reduce communication efficiency and may even affect the communication of local primary users.When the UAV swarm is in an adversarial environment,the adversary’s high-precision search and guidance radars will pose a threat to the UAVs,hindering the normal execution of tasks.Therefore,it is of great research significance and practical value to improve the target data collection efficiency and channel resource utilization of UAV swarms in electronic countermeasure environments,and to ensure their safety and smooth task execution.Based on the above analysis,the main research content and innovation points of this paper include the following three aspects:A UAV clustering method based on improved cluster head selection weight is proposed: When the spectrum resources are scarce in the environment,the congestion and conflicts of communication channel are key factors affecting the efficiency of UAV swarm task execution.How to manage UAV swarm communication to reduce conflict rate and increase data collection rate while ensuring communication quality is an urgent problem to be solved.Existing researches generally adopt clustering methods that only consider distance and communication energy consumption,ignoring the influence of flight energy consumption and available channels.As a result,their clustering strategies are difficult to meet the needs of real-time dynamic tasks.To address the above problems,this paper proposes a UAV clustering method based on improved cluster head selection weight.The core of the method is to improve and perfect the weight factors based on which the cluster head is selected,including three factors: the relative distance factor between the UAV and the target point appearing in its search range,the common channel resources factor between the UAV and its neighbors,and the remaining energy factor which considers the energy consumption of communication and flight.Secondly,based on the task of searching target data,this paper designs a complete process for building UAV clusters around the target,and assigns time slot usage order to cluster members using the time-division multiple access mechanism to ensure orderly communication.Finally,this paper further improves the update and maintenance method of UAV clusters from the aspects of the number of cluster members,energy threshold,and the appearance of primary users and new channel resources to ensure the stability of UAV clusters.Experiments show that compared with traditional clustering methods and non-clustering methods,the proposed method can reduce the communication conflict rate with primary users by about 25%,shorten the data collection time by about 9%,and increase the total amount of target point data by about26%.A joint electromagnetic and kinetic weapon resource allocation scheme based on differential particle swarm optimization algorithm is designed: When encountering enemy radar threats during task execution,the use of weapons by a UAV swarm to counter different radar targets is a key factor affecting overall combat effectiveness.How to allocate weapon resources to maximize combat effectiveness while reducing radar detection threats to the UAV swarm and ensuring smooth task execution is an urgent problem to be solved.Existing researches generally only consider the use of a single electromagnetic or kinetic weapon,ignoring the use of both weapons in combination to counter radar threats.And the attackers and defenders are limited to one-to-one and one-to-many scenarios,lacking many-to-many forms.In addition,existing optimization algorithms will appear continuous values in the solution process,which will affect the discrete decision allocation.To address these problems,this paper proposes a joint electromagnetic and kinetic weapon resource allocation method based on a differential particle swarm optimization algorithm.The core of the method is to fuse and improve the traditional particle swarm optimization algorithm and differential evolution algorithm,and introduce discretization processing and constraint condition normalization operations to enable the algorithm in this paper to effectively handle discrete decision allocation problems.Secondly,in the joint optimization allocation of electromagnetic and kinetic weapon resources,we construct a joint weapon resource allocation objective function.The objective function comprehensively considers the performance and cost corresponding to the two weapons,and can set a preference for weapon resource allocation by adjusting weight coefficients and introducing additional terms.The experiment shows that,compared to existing optimization algorithms,the differential particle swarm optimization algorithm proposed in this paper can increase the optimal objective function value during each flight by more than 3% on average,reduce the number of iterations required for the first appearance of the optimal objective function value by more than 37% on average,and shorten the time required for convergence of all decision matrices by more than 30% on average.Design and implementation of a simulation platform framework for cooperative tasks of UAV swarms: When operating in hazardous and challenging real-world environments,human-machine casualties and cost losses are two critical factors affecting the completion of UAV swarm tasks.Reducing human-machine casualties as well as cost losses during the execution of cooperative tasks by UAV swarms through simulation is an urgent problem that needs to be addressed.Existing simulation platform frameworks have relatively fixed settings for UAV swarm tasks,with intermittent and discontinuous multi-task execution,and lack of strategic assistance.To address these problems,this paper designs and implements a UAV swarm cooperative task simulation platform framework.The platform framework is driven by the task scenarios of the first two studies in this paper and demonstrates three specific tasks: search for target data,penetrate and interfere with the denied area after formation,and attack on designated targets.And the platform framework enhances the visualization effect of the demonstration by loading Google satellite maps and target entity icons.Secondly,the efficiency of target data collection is improved by applying individual search,neighbor sharing,and collaborative tracking and search strategies.Then,during the UAV flight process,three strategies,including mirror reflection adjustment,directional vector adjustment,and obstacle avoidance adjustment,are used to ensure that UAVs work within the scene range,fly to the target location,and avoid the denied zones and obstacles along the way.Evaluation indicators show that the UAV swarm simulation platform framework proposed in this paper can use pre-configured strategies to improve the performance of data collection,achieving an average target data volume and total data volume of 97%.During the penetration and interference stage,the fluctuation of the number of UAVs deduced by the platform framework is highly consistent with the theoretically derived results,with a consistency of more than 73%.