基于局部最优点的GNSS分布式压制干扰源部署算法

GNSS Distributed Interference Source Deployment Algorithm Based on Locally Optimal Point

为实现对特定区域卫星导航信号进行有效管制,通常可以使用压制干扰源对全球卫星导航系统(Global Navigation Satellite System,GNSS)实施导航干扰,而GNSS干扰源的部署方案将直接决定能否取得理想干扰效果。以单GNSS干扰源可视域分析方法为基础,研究GNSS干扰源分布式部署算法,在对传统排列组合算法分析的基础上提出计算效率更高的全局搜索算法。当任务区域增大时,为了进一步提高算法效率,在全局搜索算法的基础上又引入局部最优点的概念,提出一种基于局部最优点的优化搜索算法,且当局部最优点等级为0阶时,全局搜索算法与基于局部最优点的优化搜索算法互为等价,从而保证两种算法的数学统一。实验分析结果表明:当任务区域较小时,与传统排列组合算法相比,采用全局搜索算法能够在结果差异不大的情况下将计算效率提升99%以上;当任务区域较大时,与全局搜索算法相比,采用不同等级局部最优点的优化搜索算法,能够进一步将计算效率提升60%~97%。

In order to effectively control the satellite navigation signals in a specific area,GNSS(Global Navigation Satellite System)can usually be suppressed by interference sources to implement navigation interference,and the deployment scheme of GNSS interference sources will directly determine whether the ideal interference effect can be achieved.The distributed deployment algorithm of GNSS interference sources is studied based on a single GNSS interference source viewshed analysis method,on the basis of the analysis of the traditional permutation and combination algorithm,a global search algorithm with higher computational efficiency is proposed.For a larger task area is,the concept of locally optimal point is introduced on the basis of the global search algorithm in order to further improve the computational efficiency of the algorithm,and an optimized search algorithm based on locally optimal point is proposed.When th elevel of locally optimal point is 0,the global search algorithm is equivalent to the optimized search algorithm based on locally optimal point,so as to ensure the mathematical unity of the two algorithms.Experimental analysis shows that,compared with the traditional permutation and combination algorithm,the global search algorithm can improve the computational efficiency by more than 99%with little difference in the results when the task area is small.Compared with the global search algorithm,the optimal search algorithm with different levels of locally optimal points can further improve the computational efficiency by 60%~97%when the task area is large.