A novel adaptive nonlinear filter-based pipelined feed-forward second-order Volterra architecture

Authors:
Haiquan Zhao;Jiashu Zhang
Affiliations:
Si-Chuan Province Key Lab of Signal and Information Processing, Southwest Jiaotong University, Chengdu, China;Si-Chuan Province Key Lab of Signal and Information Processing, Southwest Jiaotong University, Chengdu, China
Venue:
IEEE Transactions on Signal Processing
Year:
2009

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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 non-linear 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.