基于改进当前统计模型的模糊自适应车辆定位算法  

Fuzzy adaptive algorithm based on modified current statistical model for vehicle positioning

作　　者：邵震洪[1,2] 李文峰[3] 吴怡[1] 杨琼[1] 沈连丰[1] 

机构地区：[1]东南大学移动通信国家重点实验室,江苏南京210096 [2]南京电讯技术研究所,江苏南京210007 [3]南京大学电子科学与工程学院,江苏南京210046

出　　处：《通信学报》2013年第7期184-190,共7页Journal on Communications

基　　金：国家高技术研究发展计划("863"计划)基金资助项目(2008AA01Z205);国家自然科学基金资助项目(61171081);江苏省技术创新基金资助项目(BC2012006);教育部新世纪优秀人才支持计划基金资助项目(NCET-10-0018)~~

摘　　要：分析比较了"当前"统计模型和Singer模型各自的特点,提出了基于改进的"当前"统计模型的模糊自适应车辆定位算法(MCS-FAEKF),实时动态选择机动模型和调整系统噪声协方差矩阵。相对于传统的"当前"统计模型卡尔曼滤波算法(CS-EKF)和Singer-EKF算法,MCS-FAEKF算法对车辆目标的定位精度和可靠性等都得到了较大提高,计算机仿真结果验证了算法的有效性和可行性。The singer model and current statistical model were first analyzed and compared. A modified scheme based on the two kinds of models was proposed. Moreover, a modified current statistical model based-fuzzy adaptive extended Kalman filter （MCS-FAEKF） algorithm was proposed to choose maneuvering model and adjust system noise covarianee dynamically. The simulated results show that the algorithm could get more accurate and reliable performance for vehicle positioning compared with the current statistical model based-extended Kalman filter （CS-EKF） and Singer-EKF algo- rithms.

关 键 词：Singer模型 “当前”统计模型 车辆定位 模糊自适应 扩展卡尔曼滤波 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]