Kalman滤波在Yule-Walker谱估计中的应用

摘要

在现代谱估计中,由于Yule-Walker沃克谱估计算法中平稳随机序列的长度n在某些情况下偏小,使计算出的ARMA随机过程的功率谱密度不能精确逼近真实值,所以我们在用自相关法估计AR模型参数时加入了kalman滤波器。将估计的AR模型系数及高斯白噪声作为滤波器的输入及部分参数,对最终估计的功率谱进行修正。实验结果表明,在Yule-Walker谱估计中加入kalman滤波,其计算精度及结果稳定性都有了一定的提高,可以作为解决相关问题的方法之一。%In modern spectral estimation,because in some cases the steady random sequence which in Yule-Walker spectrum estimated is short,and the power spectral density of the ARMA random process is hard to approaches the real density,so kalman filtering was introd