基于混沌粒子群的无人机组网导航信源筛选

Data Source Selection for UAVs'Networked Navigation System Based on Chaos Particle Swarm Optimization

从组网导航系统选取信源无人机的需求出发,研究了集群无人机的测距定位模型和信源筛选问题,基于粒子群算法(PSO)和混沌理论,提出了一种混沌粒子群优化算法(CPSO)。利用相对距离数据建立无人机离散线性定位模型,信源筛选算法采用几何精度因子(GDOP)作为适应度函数,通过速度-位置更新公式的不断迭代,使估计结果逐渐趋向最优,另外,通过混沌映射处理初始种群,避免了PSO容易陷入局部最优解的问题。仿真结果表明,所提算法解算精度高于PSO,计算耗时仅为遍历法的14.6%,在一定范围内增加学习因子的值可以提高算法的解算效率。

In view of the requirements of selecting the data source UAV in the networked navigation system,the ranging positioning model and data source selection problem of the clustered UAVs are studied.Based on Particle Swarm Optimization(PSO) and the chaos theory,a Chaos Particle Swarm Optimization(CPSO) is proposed.The relative distance is used to establish the discrete linear positioning model of the UAV,and the data source selection algorithm takes the Geometric Dilution of Precision(GDOP) as the fitness function.The estimation result is gradually optimized through the iteration of the velocity-position update equation.The initial population is processed by chaotic mapping,which avoids the problem of falling into the local optimal solution.The simulation results show that the proposed algorithm has higher calculation precision than PSO,and the computation time is only 14.6% of that of the traversal method.In addition,increasing the value of the learning factor within a certain range can improve the algorithm’s calculation efficiency.