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\iteman{io-port 00984006}
\itemau{Frank, Walter A.}
\itemti{An efficient approximation to the quadratic Volterra filter and its application in real-time loudspeaker linearization.}
\itemso{Signal Process. 45, No.1, 97-113 (1995).}
\itemab
Summary: Nonlinear filtering based on the Volterra series expansion is a powerful and popular approach in signal processing. However, a serious problem is the increased filter complexity as compared to linear filtering. This paper presents an efficient approximation to the second-order Volterra filter. The proposed filter structure, called multi-memory decomposition (MMD), is composed of three linear FIR filters and one multiplier. Hence, the number of required filter operations is linear in the filter memory length. MMD coefficient determination with respect to a second-order reference kernel is presented. Additionally, block-oriented and adaptive algorithms are proposed which calculate the filter weights from input and output measurements of an unknown system. The good performance of the MMD model is demonstrated by simulations and in a real-time application. Therefore, the linearization scheme for the compensation of nonlinear distortions with a preprocessor is introduced. The preprocessor was implemented on a DSP system to reduce the nonlinear distortions of electrodynamic loudspeakers in real-time. The results show the gain in performance if an MMD filter is used instead of a Volterra filter. This is due to the fact that with a given computational power longer memory lengths can be achieved by the MMD model.
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\itemcc{}
\itemut{Nonlinear filtering; Nonlinear system modelling; Nonlinear approximation; Quadratic Volterra filter; Adaptive filters; Nonlinear distortions; Linearization}
\itemli{doi:10.1016/0165-1684(95)00044-E}
\end