自动化学报2006，Vol.32Issue(1)：28-37,10.
基于期望最大化算法的自适应噪声交互多模型滤波
Expectation-maximization (EM) Algorithm Based on IMM Filtering with Adaptive Noise Covariance
摘要
Abstract
A novel method under the interactive multiple model (IMM) filtering framework is presented in this paper, in which the expectation-maximization (EM) algorithm is used to identify the process noise covariance Q online. For the existing IMM filtering theory, the matrix Q is determined by means of design experience, but Q is actually changed with the state of the maneuvering target.Meanwhile it is severely influenced by the environment around the target, i.e., it is a variable of time. Therefore, the experiential covariance Q can not represent the influence of state noise in the maneuvering process exactly. Firstly, it is assumed that the evolved state and the initial conditions of the system can be modeled by using Gaussian distribution, although the dynamic system is of a nonlinear measurement equation, and furthermore the EM algorithm based on IMM filtering with the Q identification online is proposed. Secondly, the truncated error analysis is performed. Finally,the Monte Carlo simulation results are given to show that the proposed algorithm outperforms the existing algorithms and the tracking precision for the maneuvering targets is improved efficiently.关键词
Interactive multiple model (IMM) filter/EM algorithm/noise covafiance identification/online parameter estimationKey words
Interactive multiple model (IMM) filter/EM algorithm/noise covafiance identification/online parameter estimation分类
信息技术与安全科学引用本文复制引用
雷明,韩崇昭..基于期望最大化算法的自适应噪声交互多模型滤波[J].自动化学报,2006,32(1):28-37,10.基金项目
Supported by the National Key Fundamental Research & Development Programs of P. R. China (2001CB309403) （2001CB309403）
