一种改进的当前统计模型自适应跟踪算法  

Improved Adaptive Current Statistical Model Tracking Algorithm

作　　者：王博[1] 贺正洪[1] 周政[1] 赵敏[1] 

机构地区：[1]空军工程大学防空反导学院,陕西西安710051

出　　处：《现代防御技术》2016年第5期34-39,共6页Modern Defence Technology

摘　　要：针对"当前"统计模型算法跟踪弱机动目标时存在较大跟踪误差,同时对机动加速度极限值存在依赖的缺陷,在分析传统"当前"统计模型适用范围的基础上,提出一种改进的"当前"统计模型自适应跟踪算法。算法首先通过泰勒级数展开和忽略高阶项的方法得到模型加速度极值和均值的修正结果,使算法能更好地描述强机动和弱机动2种情况下目标的机动特征。之后,通过引入强跟踪滤波器,进一步增强模型对目标突发机动的自适应跟踪能力。最后,针对具有周期性运动特征的跳跃巡航目标,与IMM,CS算法仿真比对,验证了算法的可行性和合理性。Aiming at large tracking error of ＂current＂statistical model tracking algorithm in tracking weak maneuvering target and the motor acceleration limit dependency defect,based on the analysis of the traditional ＂current＂statistical model,an improved ＂current＂statistical model adaptive tracking algorithm is proposed. Firstly,the correction model acceleration extremes and mean are obtained by Taylor series expansion and ignoring the higher order terms,so that the algorithm can better describe the characteristics of goals in strong motor and weak motor circumstances. And then,through the introduction of strong tracking filter,the model sudden maneuvering target adaptive tracking capability is enhanced. Finally,aiming at jump cruise targets periodic motion characteristics and comparing with IMM,CS comparison algorithm simulation,the feasibility and rationality of the algorithm are verified.

关 键 词：CS模型 目标跟踪 强跟踪滤波 加速度修正 机动频率 

分 类 号：TN820.4[电子电信—信息与通信工程] TP301.6[自动化与计算机技术—计算机系统结构]