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....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.展开更多
TDOA(Time Difference of Arrival)测距方式是UWB(Ultra Wide Band)室内定位的常用方法,针对其不可避免的随机误差以及目标改变运动状态定位不准确的问题,文中提出了一种Chan-Taylor-IMMKF(Interacting Multiple Model Kalman Filter)...TDOA(Time Difference of Arrival)测距方式是UWB(Ultra Wide Band)室内定位的常用方法,针对其不可避免的随机误差以及目标改变运动状态定位不准确的问题,文中提出了一种Chan-Taylor-IMMKF(Interacting Multiple Model Kalman Filter)定位算法。该算法由Chan-Taylor加权算法与加入自适应算法IMM卡尔曼滤波算法组成,通过Chan-Taylor加权算法初次取得目标估计坐标,将该坐标值作为自适应算法IMM的卡尔曼滤波器的测量值,对目标坐标进行多次滤波处理,最终加权得到目标的最终估计坐标。实验将该算法与未滤波的Chan-Taylor加权算法、使用传统的卡尔曼滤波算法进行对比,结果显示该算法有效减小了系统的随机误差,并克服了传统卡尔曼滤波器在目标忽然改变运动状态时不能及时跟踪从而产生较大误差的问题,将误差标准差均值控制在15 cm之内。展开更多
基金Supported by the National Key Fundamental Research & Development Programs of P. R. China (2001CB309403)
文摘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.
