2 research outputs found
On guaranteed estimation of parameters in autoregressive process with unknown noise variance
Π Π°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΡΡΡ ΠΌΠΎΠ΄ΠΈΡΠΈΠΊΠ°ΡΠΈΡ ΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΡΡ
ΠΎΡΠ΅Π½ΠΎΠΊ, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠ°Ρ ΡΠΌΠ΅Π½ΡΡΠΈΡΡ ΠΎΠ±ΡΠ΅ΠΌ Π²ΡΠ±ΠΎΡΠΊΠΈ, Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΠΉ Π΄Π»Ρ ΠΏΠΎΠ»ΡΡΠ΅Π½ΠΈΡ ΠΎΡΠ΅Π½ΠΎΠΊ Ρ Π·Π°Π΄Π°Π½Π½ΠΎΠΉ ΡΡΠ΅Π΄Π½Π΅ΠΊΠ²Π°Π΄ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΎΡΠ½ΠΎΡΡΡΡ. Π£ΠΌΠ΅Π½ΡΡΠ΅Π½ΠΈΠ΅ ΠΎΠ±ΡΠ΅ΠΌΠ° Π²ΡΠ±ΠΎΡΠΊΠΈ Π΄ΠΎΡΡΠΈΠ³Π°Π΅ΡΡΡ Π·Π° ΡΡΠ΅Ρ Π²Π²Π΅Π΄Π΅Π½ΠΈΡ Π΄ΠΎΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠ°Π³Π° Π΄Π»Ρ ΠΎΡΠ΅Π½ΠΈΠ²Π°Π½ΠΈΡ Π΄ΠΈΡΠΏΠ΅ΡΡΠΈΠΈ ΡΡΠΌΠ° ΠΏΡΠΎΡΠ΅ΡΡΠ°. ΠΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΠΌΠΎΠ΄ΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΏΡΠΎΡΠ΅Π΄ΡΡ ΠΎΡΠ΅Π½ΠΈΠ²Π°Π½ΠΈΡ Π²Π΅ΠΊΡΠΎΡΠ° ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² ΠΈ ΠΏΠΎΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΠ½ΠΎΠ³ΠΎ ΠΎΡΠ΅Π½ΠΈΠ²Π°-Π½ΠΈΡ. ΠΡΠΈΠ²ΠΎΠ΄ΡΡΡΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΡΠΈΡΠ»Π΅Π½Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ
Novel parameter estimation of autoregressive signals in the presence of noise
This paper proposes a new method for estimating the parameters of an autoregressive (AR) signal from observations corrupted by white noise. The feature of the new method is that the observation noise variance estimate is converted into the only solution of a nonlinear equation to yield unbiased estimate of the AR parameters. Moreover, a convergent Newton iterative algorithm with a deterministic initial point is presented for efficient implementation of the proposed new estimation method. As a result, the proposed new method can minimize the error of estimating the variance of the observation noise. Since more accurate estimates of this observation noise variance can be attained at earlier stages, the proposed method can achieve a good performance in estimating the AR signal parameters. Numerical results demonstrate that the proposed new algorithm is more effective in terms of accuracy and robustness against noise than conventional algorithms