Kalman滤波在Yule-Walker谱估计中的应用
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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 introduced to deal with this problem when we estimates the AR model parameter with the autocorrelation. To estimates the finally power spectrum,we take the AR model parameter and the white gaussian noise as filter's input and partial parameters. The experimental result indicated that in the Yule-Walker spectrum estimated that joins the kalman filter,its computational accuracy and stability had certain enhancement,it can be one of the methods to resolve relevant issues.
引文
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