面向弹载飞行器网络的多约束条件下部署模型

摘要

Unknown factors(usually referred to weather conditions)will make several MUAVs thrown far away from the goal airspace,Combined with requirements for collision avoiding,formation matching in cooperative maneuvering. a multi-constrained charged particle-swarm optimization two-stage deployment(MCC-PSO-TSD)algorithm is put forward to suit our background of MUAVN,in order to solve the problem encountered in the process of MUAVN deployment. This stratgy dynamically integrate constraints,including collision factor,formation factor,clumping factor and interference factor,etc. as the fuzzy measure parameter,and take the fuzzy integral as the fitness evaluation function of the charged particle swarm optimization. Simulation results show that,the proposal of the degree of looseness makes MCC-PSO-TSD perform better than those traditional VFA-based and PSO-based deployment algorithms in aspects of efficiency,adaptability,network convergence speed, evenness of coverage,etc. Especially the degree of deviation and target is larger and MUAVs' distribution is looser,the performance of MCC-PSO-TSD is more significant.%未知因素(常指气象条件)的影响会导致弹载无人飞行器(MUAV)的抛撒偏离目标空域,结合弹载飞行器网络(MUAVN)部署过程中防碰、编队等协同机动的要求,提出多约束条件下基于带电粒子群算法的两级部署策略(MCC-PSO-TSD),解决MUAVs的抛撒偏离目标空域的问题.该策略动态整合碰撞因子、编队因子、聚集因子以及干扰因子等约束条件作为模糊测度参数,并将模糊积分作为带电粒子群算法的适应度评估函数.仿真实验结果表明,MCC-PSO-TSD相比基于传统粒子群及虚拟力的部署策略更为高效、算法适应性大大增强,网络收敛速度、覆盖率以及均匀度等性能更为有效.