| 1. |
- Carlsson, B, et al.
(författare)
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A NOTCH FILTER BASED ON RECURSIVE LEAST-SQUARES MODELING
- 1994
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Ingår i: Signal Processing. - : Elsevier BV. - 0165-1684 .- 1872-7557. ; 35:3, s. 231-239
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a notch filter based on recursive least-squares sinusoidal modelling. This gives analytical insight both into least-squares modelling of sine waves in noise and the use of constrained notch filters. Especially, the derived filter corresponds to a commonly used notch filter with constrained poles and zeros.
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| 2. |
- Carlsson, B., et al.
(författare)
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Analysis and interpretation of a constrained notch filter
- 1992
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Ingår i: Acoustics, Speech, and Signal Processing, 1992. ICASSP-92., 1992 IEEE International Conference on. ; , s. 293-296
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Konferensbidrag (refereegranskat)abstract
- A notch filter based on discounted recursive least squares sinusoidal modeling is presented. This gives analytical insight both into least squares modeling of sine waves in noise and the use of constrained notch filters. Especially, it is shown that the discounted least squares solution converges to a commonly used notch filter with constrained poles and zeros, where the forgetting factor in the criterion corresponds to the pole radii in the filter. Also a Kalman filter interpretation is given for the derived constrained notch filter
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| 3. |
- Händel, Peter, 1962-, et al.
(författare)
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Adaptive estimation for periodic signal enhancement and tracking
- 1994
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Ingår i: International journal of adaptive control and signal processing (Print). - : Wiley. - 0890-6327 .- 1099-1115. ; 8:5, s. 447-456
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Tidskriftsartikel (refereegranskat)abstract
- A fast algorithm for enhancement and tracking of periodic signals with fixed or slowly varying central frequency in additive noise is proposed. The algorithm is based on results from discounted least squares identification of a harmonic signal model combined with a recursive update scheme for the estimated fundamental frequency. In particular the gain sequence of the algorithm is determined by the fundamental frequency and an eligible forgetting factor. The performance of the algorithm is compared with theoretical lower bounds and it is shown that the algorithm yields good results.
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| 4. |
- Händel, Peter, 1962-, et al.
(författare)
-
Asymptotic variance of the AR spectral estimator for noisy sinusoidal data
- 1994
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Ingår i: Signal Processing. - : Elsevier BV. - 0165-1684 .- 1872-7557. ; 35:2, s. 131-139
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Tidskriftsartikel (refereegranskat)abstract
- In this paper the autoregressive (AR) spectral estimator is analyzed in the case of noisy sinusoidal data. An expression for the large-sample normalized variance is derived and studied in detail for increasing model order. In particular, a very simple formula is derived for the asymptotic (in both number of observed data and model order) normalized variance, which confirms a conjecture made by Sakai.
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| 5. |
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| 6. |
- Händel, Peter, 1962-
(författare)
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High-order Yule-Walker estimation of the parameters of exponentially damped cisoids in noise
- 1993
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Ingår i: Signal Processing. - 0165-1684 .- 1872-7557. ; 32:3, s. 315-328
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Tidskriftsartikel (refereegranskat)abstract
- An approach for the estimation of the frequencies and damping factors of exponentially damped cisoids (complex sinusoids) is presented. The method may be seen as an extension of the method of backward linear prediction and singular value decomposition of Kumaresan and Tufts to the second-order statistics domain. The proposed estimator is interpreted as a high-order Yule-Walker (HOYW) method using a data based covariance matrix. The HOYW method is analysed at high SNR where closed-form expressions for the accuracy of the estimates are derived. By Monte Carlo simulations the HOYW method is applied to data consisting of one and two damped cisoids in additive white noise. The simulation results are compared with the results using the Kumaresan and Tufts method, with the Cramer-Rao bound, and with the derived theoretical results. The method is not statistically efficient, but the comparison shows that the HOYW method outperforms the method of Kumaresan and Tufts in terms of accuracy versus algorithmic complexity and in terms of precision in the cases considered. Due to the above properties the method is suitable to provide fast initial estimates for nonlinear search methods.
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| 7. |
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| 8. |
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| 9. |
- Händel, Peter, 1962-, et al.
(författare)
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Tracking analysis of a constrained adaptive notch filter
- 1991
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Ingår i: Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference on. ; , s. 3209-3212
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Konferensbidrag (refereegranskat)abstract
- The asymptotic tracking properties of an adaptive notch filter with pole-zeros constraints for the cancellation or retrieval of multiple time-varying sine waves in additive noise are analyzed. The methods of Ljung and Gunnarsson (see Automatica, vol.26, no.1, p.7-21, Jan. 1990) are used in order to analyze the asymptotic mean-square parameter error, when the variations in the underlying frequencies and the gain of the algorithm are assumed to be sufficiently small. A closed-form expression for the mean square error (MSE) is derived as a function of the tuning variables of the algorithm. The results give insight into the operational properties of the algorithm and are used in order to minimize the MSE with respect to the tuning variables
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| 10. |
- Händel, Peter, 1962-, et al.
(författare)
-
Tracking analysis of an adaptive notch filter with constrained poles and zeros
- 1994
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Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-587X .- 1941-0476. ; 42:2, s. 281-291
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Tidskriftsartikel (refereegranskat)abstract
- This paper analyzes the asymptotic tracking properties of an adaptive notch filter (ANF) with pole-zero constraints [1] for the cancellation or retrieval of multiple time-varying sine waves in additive noise. The asymptotic mean square error (MSE) is analyzed using the methods of Ljung and Gunnarsson [2] when the variations in the underlying frequencies are assumed to be sufficiently small. Closed-form expressions for the MSE are derived as functions of the tuning variables of the algorithm. The results give insight into the operational properties of the algorithm and are used in order to minimize the MSE with respect to the tuning variables. Computer simulations confirm the validity of the derived results.
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