文献类型：会议论文

英文题名：Extended Target Tracking using an IMM Based Nonlinear Kalman Filters

作者：Zhou, Yucheng[1] Xu, Jiahe[1] Jing, Yuanwei[2] Dimirovski, Georgi M.[3,4]

第一作者：周玉成

通信作者：Zhou, YC[1]

机构：[1]Chinese Acad Forestry, Inst Wood Ind, Dept Res, Fac Engn, Beijing 100091, Peoples R China;[2]Northeastern Univ, Fac Informat Sci & Engn, Shenyang AH-110004, Liaoning, Peoples R China;[3]Dogus Univ, Fac Engn, TR-34722 Istanbul, Turkey;[4]Saints Cyril & Methodius Univ Skopje, Fac FEIT, MK-1000 Skopje, North Macedonia

会议论文集：American Control Conference

会议日期：JUN 30-JUL 02, 2010

会议地点：Baltimore, MD

语种：英文

外文关键词：Clutter (information theory) - Extended Kalman filters

年份：2010

摘要：The unscented Kalman filter (UKF) and ensemble Kalman filter (EnKF) are developed to extended target tracking problem for high resolution sensors. The nonlinear Kalman filters are based on an ellipsoidal model, which is proposed to exploit sensor measurement of target extent. The ellipsoidal model can provide extra information to enhance tracking accuracy, data association performance, and target identification. In contrast to the most commonly used extended Kalman filter (EKF), the UKF and EnKF provide more accurate and reliable estimation performance, due to the presence of high nonlinearity of the model. Correspondingly, the EnKF has lower computational complexity than the UKF. An interacting multiple model (IMM) technique is combined with the filters to adapt the target maneuver and motion mode switching problem which is vital for nonlinear filtering. The developed IMM-UKF and IMM-EnKF algorithms on extended target tracking problem are validated and evaluated by computer simulations.