随机连续系统基于Laguerre多项式逼近的Markov参数估计

Markov Parameter Estimation for Stochastic Continuous Systems via Laguerre Polynomials

先讨论基于Ｌａｇｕｅｒｒｅ多项式逼近、有连续Ｗｉｅｎｅｒ过程扰动的随机连续线性系统最小二乘参数估计，然后讨论Ｗｉｅｎｅｒ过程的Ｌａｇｕｅｒｒｅ多项式逼近值的相关性和最小二乘估计的有偏性，在此基础上，提出无偏一致的Ｍａｒｋｏｖ估计（最小方差估计）算法，仿真结果显示本文方法的有效性．

Firstly,the least-squares parameter estimation for stochastic continuous linear systems disturbed with continuous Wiener process via Laguerre polynomial approximation is given then the corrlativeness of the polynomial approximating values of Wiener process and the biasedness of the least-squares estimation are dis-cussed. Based on the above analysis,the unbiased consistence Markov estimation method is proposed,Finally,simulation results show the effectiveness of these methods.