Mission Planning Method Of Multi-UAV Cooperative TDOA Positioning

With the development of control theory and computer science,in order to meet the increasingly complex requirements of battlefield environments,the combat mode of UAV has gradually transferred from single-platform operations to cluster operations.Compared with traditional single-platform operations,cluster operations have a high degree of intelligence and good robustness,and can better complete combat missions in complex battlefield environments.There are three key research contents in the mission planning of UAV cluster operations:(1)use the advantages of cluster operations to complete cooperative tasks;(2)path planning of cluster operations;(3)task assignment of cluster operations.These three points determine the advantages and disadvantages of UAV cluster operations,and are the key to the shift from theory to practice in cluster operations.When cluster UAVs perform cooperative tasks,they need to analyze the advantages of cooperative execution tasks compared with single-machine execution tasks and how to coordinate the cooperation between UAVs participating in cooperative tasks.Path planning and task assignment are the core issues that any mission planning system needs to be concerned about.In a complex battlefield environment,how to allocate tasks reasonably,how to design a better path for each UAV in the cluster under the premise of satisfying various constraints,how to reasonably build models and design effective algorithms to solve cluster operations,these difficult problems are worth pondering.In this paper,the reconnaissance and positioning of target are used as combat tasks of multiple UAVs.The UAVs participating in the cluster are divided into multiple formations to perform tasks separately.Based on this background,a system model for mission planning is established and the algorithm is designed and simulated.The main content of the paper is summarized as follows:1.Briefly introduce the principle of passive multi-station cooperative Time Difference of Arrival(TDOA)positioning,and point out that the layout of receiving stations in TDOA positioning greatly affects the positioning accuracy.In response to this problem,this paper proposes to use particle swarm optimization to optimize the observation points of the auxiliary UAV in the formation at each observation time,and then use the cubic spline interpolation method to connect the observation points to obtain the smooth path of the auxiliary UAV in the reconnaissance interval.2.For the path of the UAV between two reconnaissance missions,in view of the shortcomings of the existing single UAV path planning model,this article analyzes and models the threats in the combat environment.Based on the algorithm,an improved A * algorithm which can make the UAV effectively avoid risks is designed.Based on the improved algorithm,this paper uses linear interpolation to further optimize the path,which reduces the risk and solves the problem of polyline path in the planning results of the A * algorithm.3.The assignment of reconnaissance tasks and the selection of the main UAV observation point at each target to be reconnaissance,this paper uses genetic algorithm to solve.In the task assignment problem,the encoding of the genetic algorithm is similar to the integer encoding used to solve the TSP problem,but multiple 0s are inserted in the encoding sequence,and the sub-sequence between each two 0s represents the tasks that a UAV formation needs to perform,and the order of execution represents the fitness function of the algorithm in terms of estimating range cost and priority of the target to be reconnaissance.The selection of observation points is a combination optimization problem.This paper determines the selectable range of observation points at each target to be reconnaissance according to the model of mission planning.The cumulative risk cost of the main UAV path represented by the combination of observation points,and the positioning accuracy determined by the positional relationship between the target and observation points represents the fitness function of the algorithm together.4.This paper establishes a simulation model for the above algorithm and performs computer simulation.The simulation results prove the rationality of the algorithm design in the system and the effectiveness of the mission planning system.