基于粒子群算法的地磁匹配航迹规划

针对无人机地磁匹配航迹规划问题,提出了一种基于粒子群优化算法的航迹规划方法.借鉴稀疏A-Star搜索算法的思想,将约束条件结合到搜索方法中,给出了基于地磁网格图的航迹节点扩展方法.结合无人机的机动性能约束和地磁匹配的自身特点,设计了一种适用于地磁匹配的航迹性能评价函数作为粒子群的适应度函数.仿真实验的结果表明:生成的航迹能够满足地磁匹配导航要求,且具有威胁规避的能力.最后,针对只在中制导段采用地磁匹配的情况提出了初步的解决方案,保证无人机顺利进入地磁匹配航迹段.

Geomagnetic matching path planning based on PSO algorithm

A path planning method based on PSO (Particle swarm optimization) algorithm is proposed to solve geomagnetic matching navigation for UAV (Unmanned aerial vehicle). Inspired by Sparse A-Star Search method, the constraints are introduced to searching method to form the expanding space of route nodes based on geomagnetic grid map. The fitness function of PSO algorithm in path planning is designed based on maneuverability of UAV and characteristics of geomagnetic matching. The results of simulation indicate that the generated path satisfies the need of geomagnetic matching navigation, and has the function of threat avoidance. Considering to the case of geomagnetic matching used in intermediate guidance stage, a scheme is presented to guarantee UAV flying into the path part of geomagnetic matching.