Tactical Missile Technology

运用粒子群算法(PSO)进行无人机三维航迹规划时,常通过引入最小威胁曲面来减小搜索空间,提高算法效率,但将威胁信息等效至数字地图中并不能准确反映威胁作用,且失去了利用地形遮蔽进行突防的优势。针对这一问题,从航迹规划的核心适应度函数出发,综合考虑火力威胁、地形威胁、高度威胁、机动性能四个方面约束,对适应度函数模型进行了分析改进。同时,针对粒子群算法易陷入局部最优的问题,提出将模拟退火粒子群算法(SAPSO)运用于航迹规划,利用模拟退火算法概率突跳能力进一步改善航迹质量。仿真结果表明,改进模型获得的三维航迹不仅满足各项约束,而且能够利用地形遮蔽进行突防,SAPSO算法改善航迹效果明显。

When using particle swarm optimization algorithm(PSO) performed 3-D route planning of UAV,the surface of minimum risk was often used to reduce the search space and improve the efficiency of the algorithm,but the threat information is equivalent to a digital map does not accurately reflect the action of threat,and also lost the advantage of using terrain masking to penetrate.To solve the problem,the fitness function models as the key of route planning was analyzed and improved by considering the fire threat,terrain threat,height threat and mobility in total four constraints.Meanwhile,aimed the problem that the PSO algorithm is easy to fall into local optimum,the simulated annealing particle swarm optimization algorithm(SAPSO) that possess probabilistic jumping property was applied to further improve the quality of the route.The results of simulation show that the 3-D route obtained by improved model not only meet the constraints,but also can use terrain masking to penetrate,the quality of route was obviously improved by SAPSO algorithm.