基于强跟踪滤波器及小波变换的非线性系统参数辨识及应用

Parameter identification of nonlinear system and its application based on strong tracking filter and wavelet transform

中文关键词  非线性系统  小波变换  强跟踪滤波器  参数辨识

英文关键词  nonlinear systems  wavelet transform  strong tracking filter  parameter identification

中文摘要

      本文采用强跟踪滤波器为主要框架, 通过线性化和状态扩展解决非线性系统时变参数和状态的估计问题. 在普通强跟踪滤波器的基础上, 以小波变换估计量测噪声, 采用滤波增益调整系数解决过跟踪问题, 给出了主要的计算公式和参数的取值方法, Monte Carlo仿真和在弹道方程参数辨识中的应用结果表明, 本方法不但对突变参数具有强跟踪能力, 在噪声方差发生变化的情况下, 仍可以对非线性参数进行准确的辨识, 状态与参数估计精度高于 普通的强跟踪滤波器.

英文摘要

      The strong tracking extended Kalman filter(STEKF) is used as the main frame and the linearization and state expansion are employed to estimate the time-varying parameters and states of nonlinear systems. Based on the general STEKF, a wavelet-transform-based filter is proposed to estimate the variance of the measurement noise, and a new filtering gain factor is utilized in STEKF to eliminate the tracking overshoot. Main formulas for calculation and the methods for selecting parameters are presented. Monte Carlo simulation and practical application in identification of ballistic parameters demonstrate that the proposed method can exactly estimate the abruptly changing parameters even when the variance of the measurement noise is time-varying. The estimation accuracy of parameters and states is higher than that of the general STEKF.