Gaussian Particle Filter for Extended Target TrackingBased on Gaussian Process Regression
Received date: 2018-04-03
Online published: 2025-05-20
迟珞珈 , 冯新喜 , 王泉 . 高斯过程回归下的扩展目标高斯粒子滤波算法[J]. 弹箭与制导学报, 2019 , 39(2) : 115 -119,124 . DOI: 10.15892/j.cnki.djzdxb.2019.02.027
For complicated estimation of extended target shape as well as the poor tracking stability of extended Kalman filtering based on random hypersurface, a Gaussian particle filter based on Gaussian process regression is proposed. Tracking for target motion state has been made through Gaussian particle filter. This algorithm adopts star convex model for the modeling of extended target and accomplishes the estimation for extended target shape through Gaussian process online learning; at the same time, tracking for the motion state of target has been made based on the strong robustness of Gaussian particle filter and the feature of well solving particle degeneracy. Simulation experiment indicates that for any target with unknown shape, the proposed algorithm can well offer its extended shape and can greatly improve the target tracking accuracy and stability.
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