A Swept-Sine Pulse Compression Procedure for an Effective Measurement of Intermodulation Distortion

The ability to produce accurate models of nonlinear systems has grown enormously, thanks to the increase in the available computing power, thus making possible to use such models in a wide range of applications. Adopting these techniques not only enables to design much more efficient systems but also increases the need for accurate and straightforward methods to monitor distortion levels, even out of the testing laboratories. In this context, efficient estimators of total harmonic distortion (HD), HD of order $n$ , and intermodulation distortion (IMD) are needed, which are among the most widely adopted indicators of nonlinearity. In this paper, we focus on the IMD, which is a robust feature indicator within the whole systems operating bandwidth, whose measurement is based on the effects produced by the nonlinear system when a composite signal feeds its input. We propose an efficient procedure to measure the IMD allowing the simultaneous evaluation of HD of order $n$ and of total HD. The proposed technique relies and extends the synchronized exponential swept-sine technique for the characterization of nonlinear systems by introducing a suitable double exponential chirp input signal. The method estimates all the distortion-level indicators by processing the response to a single, short-duration signal, showing a moderate computational cost and low sensitivity to ambient noise. These advantages make the application of the proposed method extremely rapid and convenient, even in the measurement of operating field devices. Tests have been carried out both in simulated and real-world scenarios, confirming the proposed approach validity.

[1]  Angelo Farina,et al.  Not-Linear Convolution: A New Approach For The Auralization Of Distorting Systems , 2001 .

[2]  Pierrick Lotton,et al.  Nonparametric Identification of Nonlinear Systems in Series , 2014, IEEE Transactions on Instrumentation and Measurement.

[3]  Leon O. Chua,et al.  Fading memory and the problem of approximating nonlinear operators with volterra series , 1985 .

[4]  Udo Zoelzer,et al.  DAFX: Digital Audio Effects , 2011 .

[5]  Francesco Piazza,et al.  Identification of Hammerstein model using cubic splines and FIR filtering , 2013, 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA).

[6]  Antonio Gliozzi,et al.  Discrimination Between Cracks and Recrystallization in Steel Using Nonlinear Techniques , 2014 .

[7]  Rik Pintelon,et al.  Linear modeling in the presence of nonlinear distortions , 2002, IEEE Trans. Instrum. Meas..

[8]  John Vanderkooy,et al.  Harmonic Distortion Measurement for Nonlinear System Identification , 2016 .

[9]  Francesco Piazza,et al.  Adaptive Identification of Nonlinear Models Using Orthogonal Nonlinear Functions , 2012 .

[10]  Marco Ricci,et al.  Pulse Compression in Nondestructive Testing Applications: Reduction of Near Sidelobes Exploiting Reactance Transformation , 2019, IEEE Transactions on Circuits and Systems I: Regular Papers.

[11]  Francesco Piazza,et al.  A Novel Measurement Procedure for Wiener/Hammerstein Classification of Nonlinear Audio Systems , 2018 .

[12]  Delphine Bard Horn loudspeakers nonlinearity comparison and linearization using volterra series , 2008 .

[13]  R. de Figueiredo The Volterra and Wiener theories of nonlinear systems , 1982, Proceedings of the IEEE.

[14]  Pierrick Lotton,et al.  Synchronized Swept-Sine: Theory, Application, and Implementation , 2015 .

[15]  Pierrick Lotton,et al.  Nonlinear System Identification Using Exponential Swept-Sine Signal , 2010, IEEE Transactions on Instrumentation and Measurement.

[16]  K. Narendra,et al.  An iterative method for the identification of nonlinear systems using a Hammerstein model , 1966 .

[17]  Yves Rolain,et al.  Identification of linear systems with nonlinear distortions , 2003, Autom..

[18]  Vito Volterra,et al.  Theory of Functionals and of Integral and Integro-Differential Equations , 2005 .

[19]  Angelo Farina,et al.  Simultaneous Measurement of Impulse Response and Distortion with a Swept-Sine Technique , 2000 .

[20]  Francesco Piazza,et al.  System Identification based on Hammerstein Models using Cubic Splines , 2013 .

[21]  Pietro Burrascano,et al.  Harmonic Distortion Estimate for Damage Detection , 2018, NAECON 2018 - IEEE National Aerospace and Electronics Conference.

[22]  Marc Rébillat,et al.  Identification of cascade of Hammerstein models for the description of nonlinearities in vibrating devices , 2011 .

[23]  Lars-Henning Zetterberg,et al.  Identification of certain time-varying nonlinear Wiener and Hammerstein systems , 2001, IEEE Trans. Signal Process..

[24]  Danilo Comminiello,et al.  Comparison of Hammerstein and Wiener systems for nonlinear acoustic echo cancelers in reverberant environments , 2011, 2011 17th International Conference on Digital Signal Processing (DSP).

[25]  Nicola Femia,et al.  Pulse Compression for Ferrite Inductors Modeling in Moderate Saturation , 2018, 2018 15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD).

[26]  Nicola Femia,et al.  A Novel Method to Predict the Real Operation of Ferrite Inductors With Moderate Saturation in Switching Power Supply Applications , 2016, IEEE Transactions on Power Electronics.

[27]  Stefania Cecchi,et al.  A pulse compression procedure for an effective measurement of intermodulation distortion , 2017, Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis.