TY - GEN
T1 - Non-iterative Subspace-based Method for Estimating AR Model Parameters in the Presence of White Noise with Unknown Variance
AU - Esfandiari, Majdoddin
AU - Vorobyov, Sergiy A.
AU - Karimi, Mahmood
PY - 2019/11
Y1 - 2019/11
N2 - We consider the problem of estimating the parameters of autoregressive (AR) processes in the presence of white observation noise with unknown variance, which appears in many signal processing applications such as spectral estimation, and speech processing. A new non-iterative subspace-based method named extended subspace (ESS) method is developed. The basic idea of the ESS is to estimate the variance of the observation noise via solving a generalized eigenvalue problem, and then estimate the AR parameters using the estimated variance. The major advantages of the ESS method include excellent reliability and robustness against high-level noise, and also estimating the AR parameters in a non-iterative manner. Simulation results help to evaluate the performance of the ESS method, and demonstrate its robustness.
AB - We consider the problem of estimating the parameters of autoregressive (AR) processes in the presence of white observation noise with unknown variance, which appears in many signal processing applications such as spectral estimation, and speech processing. A new non-iterative subspace-based method named extended subspace (ESS) method is developed. The basic idea of the ESS is to estimate the variance of the observation noise via solving a generalized eigenvalue problem, and then estimate the AR parameters using the estimated variance. The major advantages of the ESS method include excellent reliability and robustness against high-level noise, and also estimating the AR parameters in a non-iterative manner. Simulation results help to evaluate the performance of the ESS method, and demonstrate its robustness.
KW - Autoregressive signals
KW - Noisy observations
KW - Subspace-based method
KW - Yule-Walker equations
UR - http://www.scopus.com/inward/record.url?scp=85083339636&partnerID=8YFLogxK
U2 - 10.1109/IEEECONF44664.2019.9048977
DO - 10.1109/IEEECONF44664.2019.9048977
M3 - Conference contribution
AN - SCOPUS:85083339636
T3 - Asilomar Conference on Signals, Systems, and Computers proceedings
SP - 1299
EP - 1303
BT - Asilomar Conference on Signals, Systems, and Computers proceedings
A2 - Matthews, Michael B.
T2 - Asilomar Conference on Signals, Systems & Computers
Y2 - 3 November 2019 through 6 November 2019
ER -