Analysis of frequency estimation MSE for all-pass-based adaptive IIR notch filters with normalized lattice structure
| Autor: | Yuki Noguchi, Shunsuke Koshita, Masahide Abe, Masayuki Kawamata |
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| Rok vydání: | 2017 |
| Předmět: |
Mathematical optimization
Mean squared error 020206 networking & telecommunications 02 engineering and technology Division (mathematics) Band-stop filter Uncorrelated Affine combination Control and Systems Engineering Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Electrical and Electronic Engineering Lattice algorithm Infinite impulse response Algorithm Software All-pass filter Mathematics |
| Zdroj: | Signal Processing. 132:85-95 |
| ISSN: | 0165-1684 |
| DOI: | 10.1016/j.sigpro.2016.09.019 |
| Popis: | This paper theoretically analyzes the Mean Square Error (MSE) on the steady-state frequency estimation realized by the all-pass-based adaptive notch filtering algorithms with the normalized lattice structure. The adaptive algorithms to be considered are the Simplified Lattice Algorithm (SLA) proposed by Regalia and the Affine Combination Lattice Algorithm (ACLA) proposed by the authors. For these two algorithms, we derive the frequency estimation MSE in closed-form. The derivation is based on construction of a linear time-invariant model for generation of frequency estimation error, and division of this model into two submodels of which output signals are statistically uncorrelated to each other. This strategy leads to more accurate theoretical MSE expressions than the direct use of the existing analysis methods. Simulation results demonstrate that our theoretical MSE expressions are in very good agreement with the simulated MSE values. HighlightsThe frequency estimate MSEs are derived in closed-form for SLA and ACLA.The proposed MSE expressions show higher accuracy than the conventional methods.The key point in MSE derivation is division of error generation models. |
| Databáze: | OpenAIRE |
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