红外鱼眼系统下的多目标威胁评估研究  

Multi-Target Threat Assessment of the Infrared Fisheye System

作　　者：周玉龙[1] 何永强[1] 张维安[1] 

机构地区：[1]军械工程学院光学与电子工程系,河北石家庄050003

出　　处：《光学学报》2012年第6期53-59,共7页Acta Optica Sinica

摘　　要：针对红外鱼眼告警系统在对几公里以外的目标成像时表现为点目标,无距离、几何形状和纹理信息可以利用,很难对来袭目标的威胁程度做出较准确的评估问题,研究了红外鱼眼系统下的多目标威胁评估排序方法。提出了一种红外鱼眼系统下的多目标威胁评估模型。该模型利用激光测距机所获取的各目标的初始距离信息推导出了目标在各时刻的距离和径向速度,从而建立起了以目标的距离、径向速度、航向角和高低角为威胁指标的多目标威胁评估模型。然后根据多目标威胁评估的非线性特点,以及神经网络在解决非线性复杂问题所具有的良好的自适应能力和自学习能力,利用径向基函数(RBF)神经网络对多目标威胁程度进行评估并讨论了训练样本的生成方法。实验结果证明了该方法的可行性和有效性。When the target is several miles away from the infrared fisheye system,it will be a point target in the infrared image,so there is no target information of distance,geometry and texture,without which it is hard to assess the threat of target accurately.So the multi-target threat assessment of infrared fisheye system is studied.A multi-target threat assessment model of the infrared fisheye system is proposed.In the model,the distance and the radial velocity of each hour are derived from the initial distance taken by laser range finder,and hence the multi-target threat assessment model is established including the threat factors of target distance,radial velocity,course angle and angular altitude.Then considering the nonlinear characteristic of multi-target threat assessment,the radial basis function（RBF） neural network is used to solve the problem for its good self-adaptive and self study ability to solve nonlinear complex problems and the training sample generation is also discussed.After simulation experiment,it is found that this method is feasible and effective.

关 键 词：成像系统 红外鱼眼系统 多目标威胁评估 威胁评估模型 径向基函数神经网络 

分 类 号：O439[机械工程—光学工程]