To address the problem of extended target tracking(ETT)with irregular shape,this paper proposes a random hypersurface model-adaptive progressive bayesian filter(RHM-APBF).First,the local cumulative distribution of the continuous state prior probability density of extended target is randomly sampled,and the optimal position of the sampling point is obtained by minimizing the modified Cramer-Von Mises distance between the local cumulative distri-bution of the continuous probability density and the Dirac mixture probability density.Then,the sampled particles are migrated to the posterior dense area to obtain a more accurate posterior probability density approximation by progres-sive update with adaptive variable step size.Furthermore,the random hypersurface model is used to represent the mea-surement source distribution of arbitrary star-convex extended targets,and an adaptive progressive filter for tracking star-convex irregular shape extended target is proposed,which effectively recurses the multi-feature probability density of irregular shape extended targets.Finally,the effectiveness of the proposed method is verified by the tracking simula-tion experiments of the extended target(ET)and group target(GT)at different noise level and complex random envi-ronment.
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