An Innovative Internal Model Adaptive Kalman Filtering Method and Its Applications
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摘要: 针对混杂有确定性扰动分量的随机信号处理问题,提出一种基于内模的自适应卡尔曼滤波新方法──-内模自适应卡尔曼滤波法.首先将待估有用信号和观测数据中的确定性扰分量分别以分段正弦曲线拟合方 式建立各自的内模,并将这些内模的参数作为增广状态变量形成新的非线性系统模型.然后采用迭代型推广卡 尔曼滤波算法,同时实现有用信号及扰动内模参数的实时跟踪.机动目标跟踪的GPS定位信号估计应用表明, 与现有方法相比新方法可显著提高定位精度.Abstract: To deal with the stochastic signal processing problem in which the deterministic disturbance is involved, a new adaptive Kalman filtering technique based on internal model (so-called Internal model adaptive kalman filtering──IMAKF) is proposed. The basic idea is as follows: First, the internal models of both the signal and the deterministic disturbance in the observed data are established by means of piecewise sine wave─fitting. Furthermore, the parameters in these internal models are taken as the augmented state variables to form a new nonlinear system model. Then, the iterative version of extended Kalman filtering (EKF) algorithm is utilized to realize the real-time tracking of those internal model parameters. The innovative approach has been successfully applied to the GPS signal estimation for the purpose of maneuvering target tracking. In comparison with the existing methods, the proposed approach can remarkably improve the positioning accuracy.
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