Introducing the Orthogonal Periodic Sequences for the Identification of Functional Link Polynomial Filters

The paper introduces a novel family of deterministic signals, the orthogonal periodic sequences (OPSs), for the identification of functional link polynomial (FLiP) filters. The novel sequences share many of the characteristics of the perfect periodic sequences (PPSs). As the PPSs, they allow the perfect identification of a FLiP filter on a finite time interval with the cross-correlation method. In contrast to the PPSs, OPSs can identify also non-orthogonal FLiP filters, as the Volterra filters. With OPSs, the input sequence can have any persistently exciting distribution and can also be a quantized sequence. OPSs can often identify FLiP filters with a sequence period and a computational complexity much smaller than that of PPSs. Several results are reported to show the effectiveness of the proposed sequences identifying a real nonlinear audio system.

[1]  M. Korenberg,et al.  Exact orthogonal kernel estimation from finite data records: Extending Wiener's identification of nonlinear systems , 1988, Annals of Biomedical Engineering.

[2]  Simone Orcioni,et al.  Identification of Volterra Models of Tube Audio Devices using Multiple-Variance Method , 2018, Journal of the Audio Engineering Society.

[3]  George-Othon Glentis,et al.  Efficient algorithms for the solution of block linear systems with Toeplitz entries , 1993 .

[4]  Giovanni L. Sicuranza,et al.  Perfect periodic sequences for Legendre nonlinear filters , 2014, 2014 22nd European Signal Processing Conference (EUSIPCO).

[5]  Giovanni L. Sicuranza,et al.  Legendre nonlinear filters , 2015, Signal Process..

[6]  Nithin V. George,et al.  Design of dynamic linear-in-the-parameters nonlinear filters for active noise control , 2016, 2016 24th European Signal Processing Conference (EUSIPCO).

[7]  Giovanni L. Sicuranza,et al.  Perfect periodic sequences for identification of even mirror fourier nonlinear filters , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[8]  W. Rudin Principles of mathematical analysis , 1964 .

[9]  Alberto Carini,et al.  Orthogonal LIP Nonlinear Filters , 2018 .

[10]  Y. W. Lee,et al.  Measurement of the Wiener Kernels of a Non-linear System by Cross-correlation† , 1965 .

[11]  Simone Orcioni,et al.  Perfect periodic sequences for nonlinear Wiener filters , 2016, 2016 24th European Signal Processing Conference (EUSIPCO).

[12]  Giovanni L. Sicuranza,et al.  Nonlinear system identification using quasi-perfect periodic sequences , 2016, Signal Process..

[13]  V. J. Mathews,et al.  Polynomial Signal Processing , 2000 .

[14]  Suleyman Serdar Kozat,et al.  A Comprehensive Approach to Universal Piecewise Nonlinear Regression Based on Trees , 2014, IEEE Transactions on Signal Processing.

[15]  Aníbal R. Figueiras-Vidal,et al.  Adaptive Combination of Volterra Kernels and Its Application to Nonlinear Acoustic Echo Cancellation , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[16]  C. Antweiler Multi‐Channel System Identification with Perfect Sequences – Theory and Applications – , 2008 .

[17]  Andrzej Milewski,et al.  Periodic Sequences with Optimal Properties for Channel Estimation and Fast Start-Up Equalization , 1983, IBM J. Res. Dev..

[18]  Steven Roman The Umbral Calculus , 1984 .

[19]  Walter Kellermann,et al.  Recent Advances on LIP Nonlinear Filters and Their Applications , 2018 .

[20]  Vasilis Z. Marmarelis,et al.  Nonlinear Dynamic Modeling of Physiological Systems , 2004 .

[21]  Simone Orcioni,et al.  Improving the approximation ability of Volterra series identified with a cross-correlation method , 2014 .

[22]  Alberto Carini,et al.  Fourier nonlinear filters , 2014, Signal Process..

[23]  M. Schetzen,et al.  Nonlinear system modeling based on the Wiener theory , 1981, Proceedings of the IEEE.

[24]  Yuriy V. Zakharov,et al.  A low-complexity RLS-DCD algorithm for volterra system identification , 2016, 2016 24th European Signal Processing Conference (EUSIPCO).

[25]  Giovanni L. Sicuranza,et al.  Perfect periodic sequences for even mirror Fourier nonlinear filters , 2014, Signal Process..

[26]  Giovanni L. Sicuranza,et al.  A study about Chebyshev nonlinear filters , 2016, Signal Process..

[27]  H. D. Luke,et al.  Sequences and arrays with perfect periodic correlation , 1988 .