EM-GMPF:一种基于EM的混合高斯粒子滤波器算法

EM-GMPF:An EM-Based Gaussian Mixture Particle Filter Algorithm

粒子滤波器算法是一种基于贝叶斯推理和蒙特卡罗方法的非线性、非高斯动态系统的实时推理算法.因其具有灵活、易于实现、并行化等特点,成为统计学、信号处理、人工智能等领域新的研究热点,并被广泛地应用于目标跟踪等领域中.粒子滤波器算法中存在的主要问题是再取样步骤带来的粒子枯竭,从粒子滤波器的表示方法角度出发,提出了一种基于EM的混合高斯粒子滤波器算法,仿真数据和可视化跟踪实验表明,与传统的粒子滤波器算法和基于单高斯模型的粒子滤波器算法相比,该方法在降低对粒子数目需求的同时显著提高了粒子滤波器的估计性能.

Particle filter is a new real time inference algorithm, which is based on Bayesian inference and Monte Carlo method. Because of its unique characteristics such as being flexible, easy to implement, and parallelizable, and being efficient for processing nonlinear problems, particle filter becomes a new and very promising hot topic in applied statistics, signal processing, and artificial intelligence communities. Moreover, it has been applied to many applications such as object tracking and etc. The biggest problem which influences the estimation performance in a particle filter is sample depletion brought by resampling step. This paper focuses on solving this problem from the representation method of particles, and an EM-based Gaussian mixture particle filter is presented. It is demonstrated by computer simulation and visual tracking that the proposed method can reduce the need of sampling numbers and improve the estimation performance of particle filter.