A Novel Adaptive Nonlinear Filter-Based Pipelined Feedforward Second-Order Volterra Architecture
ABSTRACT Due to the computational complexity of the Volterra filter, there are limitations on the implementation in practice. In this paper, a novel adaptive joint process filter using pipelined feedforward second-order Volterra architecture (JPPSOV) to reduce the computational burdens of the Volterra filter is proposed. The proposed architecture consists of two subsections: nonlinear subsection performing a nonlinear mapping from the input space to an intermediate space by the feedforward second-order Volterra (SOV), and a linear combiner performing a linear mapping from the intermediate space to the output space. The corresponding adaptive algorithms are deduced for the nonlinear and linear combiner subsections, respectively. Moreover, the analysis of theory shows that these adaptive algorithms based on the pipelined architecture are stable and convergence under a certain condition. To evaluate the performance of the JPPSOV, a series of simulation experiments are presented including nonlinear system identification and predicting of speech signals. Compared with the conventional SOV filter, adaptive JPPSOV filter exhibits a litter better convergence performance with less computational burden in terms of convergence speed and steady-state error.
- SourceAvailable from: Lu Lu
Digital Signal Processing 08/2015; DOI:10.1016/j.dsp.2015.08.004 · 1.50 Impact Factor
- "To solve the computational complexity problem, some truncated Volterra filters were proposed, such as second-order Volterra (SOV)  and thirdorder Volterra (TOV) . The truncated Volterra filters have been widely applied to estimate and identify many nonlinear dynamic systems    , equalization and compensation of channel systems , image processing  and speech processing  . "
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