Deterministic regression methods for unbiased estimation of time-varying autoregressive parameters from noisy observations
Deterministic regression methods for unbiased estimation of time-varying autoregressive parameters from noisy observations
复制标题
DOI：
10.1016/j.sigpro.2011.09.020
复制
发表时间：
2012-04
期刊：
影响因子：
--
通讯作者：
H. Ijima;É. Grivel
中科院分区：
文献类型：
--
作者：
H. Ijima;É. Grivel
在无噪声或观测数据受随机噪声干扰的情况下，自回归参数估计一直受到人们的关注。跟踪时变自回归（TVAR）参数也已讨论，但很少有文章处理这个问题时，有一个加性零均值白色高斯测量噪声。在本文中，人们认为确定性回归方法（或进化方法）的TVAR参数被假定为加权组合的基函数。然而，当使用标准最小二乘法时，加性白色测量噪声导致权重估计偏差。因此，我们提出了两种可选的盲离线方法，允许加性噪声的方差和权重估计。第一个是基于变量误差的问题，而第二个包括在查看的估计问题作为一个广义特征值问题。与其他现有方法的比较研究证实了所提出的方法的有效性。
A great deal of interest has been paid to autoregressive parameter estimation in the noise-free case or when the observation data are disturbed by random noise. Tracking time-varying autoregressive (TVAR) parameters has been also discussed, but few papers deal with this issue when there is an additive zero-mean white Gaussian measurement noise. In this paper, one considers deterministic regression methods (or evolutive methods) where the TVAR parameters are assumed to be weighted combinations of basis functions. However, the additive white measurement noise leads to a weight-estimation bias when standard least squares methods are used. Therefore, we propose two alternative blind off-line methods that allow both the variance of the additive noise and the weights to be estimated. The first one is based on the errors-in-variable issue whereas the second consists in viewing the estimation issue as a generalized eigenvalue problem. A comparative study with other existing methods confirms the effectiveness of the proposed methods.