Component Pruning in Gaussian Mixture Implementation of Probability Hypothesis Density
概率假设密度高斯混合实现的分量删减

As far as component pruning in Gaussian mixture (GM) implementation of probability hypothesis density (PHD) is concerned, a component pruning algorithm based on Dirichlet distribution is proposed to improve the performance of Gaussian mixture implementation of probability hypothesis density. The maximum a posterior criterion is adopted for estimation of mixing parameters. Dirichlet distribution with negative exponent parameters, which only depends on mixing weights, is adopted as the prior distribution of mixing parameters. The update formulation of mixing weight is derived by Lagrange multiplier. The instability of Dirichlet distribution with negative exponent parameters is applied to driving the components irrelevant with target intensity to extinction during the maximum a posterior iteration. Besides, the problem that one peak of intensity is presented by several proximate mixing component, can be solved by this instability. It is useful for the following state extraction. Simulation results show that the component pruning algorithm based on Dirichlet distribution is superior to that of typical Gaussian mixture implementation.