摘要
针对多运动平台多传感器背景下的机动目标跟踪问题,提出了模糊自适应无迹卡尔曼滤波(FUKF)算法,基于良好机动性描述的"当前统计"模型和良好非线性滤波能力的UKF(Unscented Kalman Filter),利用模糊推理技术实时自适应地调整系统噪声协方差矩阵,来提高对目标机动性的理解能力和跟踪精度.仿真实验表明该算法的有效性.
For multiple moving platform multisensor target tracking,a novel fuzzy adaptive Unscented Kalman Filter(FUKF)algorithm based on current statistical model favorably describing maneuvering targets and UKF possessing nonlinear filtering ability is presented,which improves the ability of comprehending target maneuvering and tracking accuracy by adjusting the system noise covariance with fuzzy inference technique.The Monte-Carlo simulation results prove its availability.
作者
盖旭刚
江晶
无
GE Xu-gang;JIANG Jing;无(Department of Information&.Command Automation,AFRA,Wuhan 430019,China;Military Representatives Of fice of Air Force in Jing Feng District,Beijing 100074;School of Electronics Information,Wuhan University,Wuhan 430079,China)
出处
《电子器件》
CAS
2007年第4期1495-1498,共4页
Chinese Journal of Electron Devices
基金
国防"十一五"预研基金项目资助(513070204)
关键词
多运动平台多传感器
机动目标跟踪
模糊推理
UKF
multiple moving platforms and sensors
maneuvering target tracking
fuzzy inference
UKF
