摘要
目标运动参数未知给移动目标搜索计划编制引入了不确定性,降低了卫星的侦察效能.为了降低不确定性和优化卫星的搜索方案,在采用贝叶斯规则对目标概率分布更新的基础上,提出一种基于高斯分布的目标转移概率密度函数,并给出相应的转移计算方法.采用最大发现概率和与最大覆盖作为搜索策略,建立移动目标搜索仿真场景.结果显示,该方法能够减少卫星搜索计划制定过程中的不确定性,降低了搜索的盲目性,提高了卫星的效能.
The unknown parameters of moving target bring uncertainty to satellite search plan making which impairs satellite search efficiency. Based on Bayesian update of target distribution, a Gaussian distribution of target transition probability function is deduced to depress the effect of uncertainty and optimize the search plan, and a corresponding method of calculating target transition probability is also introduced. A simulation scenario for moving target search by satellite with maximum sum of detection probability algorithm and maximum coverage algorithm is utilized to testify the target prediction method. The simulation performance statistics show that the prediction algorithm is able to decrease the uncertainty, reduce the eyeless search when making the satellite search plan, and improve the efficiency.
出处
《控制与决策》
EI
CSCD
北大核心
2009年第7期1007-1012,共6页
Control and Decision
基金
国家自然科学基金项目(70601035)
关键词
移动目标搜索
运动预测
贝叶斯规则
高斯分布
概率密度函数
Moving target search
Motion prediction
Bayes＇rule
Gaussian distribution
Probability density function
