姿态角辅助IMMCKF算法

Chen Hai, Shan Ganlin
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引用次数: 1

摘要

为了有效地解决非线性机动目标的跟踪问题,在多模型相互作用(imm)算法中引入了多模型相互作用(imm)算法中的cubature Kalman filter (ckf)。本文将姿态角信息引入immckf算法,通过姿态角与目标当前运动模式的模糊关联来识别目标机动模式;然后将关联结果与imm的模型概率进行融合,增强其模型分辨能力。机动目标跟踪仿真结果表明,姿态角辅助immckf (aa-immckf)算法能有效提高原immckf算法的跟踪精度和稳定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Attitude angle aided IMMCKF algorithm
To effectively solve the tracking problem of nonlinear maneuvering target, interacting multiple model cubature Kalman filter (immckf) algorithm brings cubature Kalman filter (ckf) into the interacting multiple model (imm) algorithm. This paper brings the attitude angle information into the immckf algorithm, and identifies the target maneuver mode through the fuzzy association between the attitude angle and the current motion mode of target; then the association result is used to fuse with the model probability of imm to enhance its model resolving power. A simulation of maneuvering target tracking shows that the attitude angle aided immckf (aa-immckf) algorithm can effectively improve the tracking accuracy and stability of the original immckf algorithm.
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