【摘要】 分别从UKF滤波器的内在机理和人手运动模型两个方面入手,以改善跟踪结果的精确度为基本目标,重点对UKF算法中存在的部分理论问题进行了探讨,在此基础上提出了改进后的UKFDUT算法,同时也对IMM进行了改进,把IMM模型变为MM模型,再进一步将UKFDUT算法和MM模型相融合,得到UKFDUT+MM算法.研究表明,Sigma点具有一些特性,通过对这些特性进行研究,可以找到改进跟踪精度的新途径;把MM模型和人手模型评价标准相结合,可以取得比单独使用IMM更好的跟踪精度.实验结果也表明了算法的有效性和令人满意的跟踪精度.更多还原

【Abstract】 On the one hand,the unscented Kalman filter(UKF) is an algorithm for recursive state estimation in nonlinear systems by transforming approximations of the distributions through the nonlinear system and observation functions.This transformation is used to compute predictions for the state and observation variables in the standard Kalman filter.In this approach,the distribution is represented by a set of deterministically chosen points,which are called sigma points.These points capture the mean and covariance of the random variables and are propagated through the nonlinear system.On the other hand,interactive multiple model(IMM) filter can deal with system parameter uncertainties and obtain better precision motions.In order to combine UKF and IMM and absorb their primes,starting with both the inherent mechanism of UKF and dynamic state models of human hand,and aiming at improving accurateness of human hand tracking,some theoretical problems unsolved in UKF are firstly discussed and a novel improved UKF based on double unscented transformation(UKFDUT) is put forward.Subsequently,IMM is modified and changed into multiple model(MM).The research results show that sigma points take on many wonderful features through which some novel approaches can be explored to improve tracking precision,and that using MM for state prediction can reach higher precision than using IMM lonely.The experimental results also demonstrate the effectiveness and satisfactory tracking results.更多还原