摘要
基于线性规划模型理论,使用传感器效能函数和匹配函数矩阵,建立了多传感器管理模型和优化目标函数,对多传感器管理的算法求解问题进行了研究,并对算法进行了仿真实验。结果表明:两种算法都可以得到较为理想的结果;基于深度优先搜索算法的效果最好,可以寻找全局最优解;基于蒙特卡罗随机算法的时效性最好,可以实现计算时间的可控,在极端条件下能够快速获取可行解。
Based on the traditional linear programming model,with sensors' efficiency function and matching matrix,the deep first search( DFS) and Monte Carlo random( MCR) were applied in the search of multi-sensor management problem. The results of mathematical analysis and simulation showed that,although both of them could solve the optimized results,they had obvious difference in efficiency and precision. While the DFS find the global optimization theoretically,the MCR could search the reliable optimization with probability of 1,and could find acceptable results in limited time. It could not be ignored that,in extreme case,the latter could find alternative array results instantaneously.
出处
《西北工业大学学报》
EI
CAS
CSCD
北大核心
2017年第S1期32-35,共4页
Journal of Northwestern Polytechnical University
关键词
多传感器管理
线性规划
深度优先搜索
蒙特卡罗
multi-sensor management
linear programming
deep first search
Monte Carlo random
