关键词: GGIW-CPHD滤波器, 衍生目标, 随机矩阵

Abstract: In clutter background, the Gamma Gaussian Inverse Wishart mixture Cardinalized Probability Hypothesis Density（GGIW-CPHD） filter is hard to extract extended target for spawning. A method is proposed to model and track spawning target by using multiple hypothesis structure. The algorithm adopts random matrices models to model the shapes and dimensions of extended target and predicted spawning events according to multi-spawning hypothesis model. It achieves combined estimation of extended target motion state, expansion state and measurement rate via GGIW mixture. Experimental results show that the proposed method can better achieve target cardinality estimation performance and has a stronger applicability in the environment containing spawning events in comparison with standard GGIW-CPHD filter algorithm.

Key words: Gamma Gaussian Inverse Wishart mixture Cardinalized Probability Hypothesis Density（GGIW-CPHD） filter, spawning target, random matrices