Time-Varying AR Spectral Estimation Using an Indefinite Matrix-Based Sliding Window Fast Linear Prediction
Time-Varying AR Spectral Estimation Using an Indefinite Matrix-Based Sliding Window Fast Linear Prediction
复制标题
DOI：
10.1587/transfun.e97.a.547
复制
发表时间：
2014-02-01
影响因子：
0.5
通讯作者：
Nishiyama, Kiyoshi
中科院分区：
文献类型：
--
作者：
Nishiyama, Kiyoshi
A method for efficiently estimating the time-varying spectra of nonstationary autoregressive (AR) signals is derived using an indefinite matrix-based sliding window fast linear prediction (ISWFLP). In the linear prediction, the indefinite matrix plays a very important role in sliding an exponentially weighted finite-length window over the prediction error samples. The resulting ISWFLP algorithm successively estimates the time-varying AR parameters of order N at a computational complexity of O(N) per sample. The performance of the AR parameter estimation is superior to the performances of the conventional techniques, including the Yule-Walker, covariance, and Burg methods. Consequently, the ISWFLP-based AR spectral estimation method is able to rapidly track variations in the frequency components with a high resolution and at a low computational cost. The effectiveness of the proposed method is demonstrated by the spectral analysis results of a sinusoidal signal and a speech signal.
