基于“当前”统计模型的模糊自适应跟踪算法  

A Fuzzy Adaptive Tracking Algorithm Based on Current Statistical Model

作　　者：胡洪涛[1] 敬忠良[1] 田宏伟[1] 胡士强[1] 

机构地区：[1]上海交通大学电子信息与电气工程学院航空航天信息与控制研究所,上海200030

出　　处：《系统仿真学报》2005年第2期293-295,299,共4页Journal of System Simulation

基　　金：国家自然科学基金(60375008);国家科技攻关计划重点项目世博科技专项(2004BA908B07);高校博士点基金(20020248);航空科学基金(02D57003);航天支撑技术基金(2003-1.3 02; JD04);上海市科技攻关重大预研项目(035115009)联合资助。

摘　　要：“当前”统计模型需要预先设定目标最大机动加速度,不能很好的适应各种机动情况。采用模糊推理的方法根据测量新息和新息变化率实时调整目标最大机动加速度,自适应各种机动情况。此外,针对多数传感器测量方程的非线性,采用性能较好的 Unscented Kalman Filter 代替常用的扩展卡尔曼滤波。仿真结果表明,该算法在跟踪精度和收敛速度都优于传统的基于“当前”统计模型的跟踪算法。Current statistical model needs to pre-define the value of maximum accelerations of maneuvering targets. So it may be difficult to meet all maneuvering conditions. The Fuzzy inference combined with Current statistical model is proposed to cope with this problem. Given the error and change of error in the last prediction, fuzzy system on-line determines the magnitude of maximum acceleration to adapt to different target maneuvers. Furthermore, in tracking problem many measurement equations are non-linear. Unscented Kalman filter is applied instead of extended Kalman filter. The Monte Carlo simulation results show that this method outperforms the conventional tracking algorithm based on current statistical model in both tracking accuracy and convergence rate.

关 键 词：模糊自适应 跟踪算法 实时 仿真结果 模糊推理 统计模型 传感器 跟踪精度 新息 扩展卡尔曼滤波 

分 类 号：V355[航空宇航科学与技术—人机与环境工程] TP273.4[自动化与计算机技术—检测技术与自动化装置]