摘要
为解决无人战斗机(unmanned combat aerial vehicle,UCAV)实时航路规划问题,通过对各种威胁等效为雷达威胁,威胁分级和每级分层次的处理方法,得到每个威胁的击毁和击伤作用距离。建立UCAV简易的二维模型,利用其飞行姿态与雷达散射截面积(radar cross section,RCS)之间的关系,得出以探测概率为基础的威胁代价函数。最后运用自适应Meta-Lamarckian学习策略的粒子群优化(particle swarm optimization,PSO)算法对方法进行实时性仿真测试,结果表明此方法的有效性。
In order to solve the problem of real-time route planning of unmanned combat aerial vehicle（UCAV）,the damage and injury distance are got through threat classification and delamination.A simple two-dimensional UCAV model is established.The threat cost function based on the detecting probability is got by utilizing the relation between flight attitude and dynamic radar cross section（RCS）.Finally the adaptive learning particle swarm optimization（PSO） algorithm is used to simulate the new way,and the result indicates the validity of this method.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2010年第8期1706-1710,共5页
Systems Engineering and Electronics
基金
国家高技术研究发展计划(863计划)(2008AA7050703)资助课题
关键词
无人战斗机
最优航路
威胁等效
动态雷达散射截面积
粒子群优化算法
unmanned combat aerial vehicle（UCAV）
best route
threat equivalent
dynamic radar cross section（RCS）
particle swarm optimization（PSO） algorithm
