Least lp-norm impulsive noise cancellation with polynomial filters
Signal Processing
Volterra Series Based Modeling and Compensation of Nonlinearties in High Power Amplifiers
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 3 - Volume 3
Novel schemes for nonlinear acoustic echo cancellation based on filter combinations
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
A novel adaptive nonlinear filter-based pipelined feed-forward second-order Volterra architecture
IEEE Transactions on Signal Processing
Nonlinear adaptive prediction of nonstationary signals
IEEE Transactions on Signal Processing
Adaptive threshold nonlinear algorithm for adaptive filters withrobustness against impulse noise
IEEE Transactions on Signal Processing
Adaptive Volterra filters for active control of nonlinear noiseprocesses
IEEE Transactions on Signal Processing
Nonlinear system identification in impulsive environments
IEEE Transactions on Signal Processing
Adaptive Cancellation of Nonlinear Intersymbol Interference for Voiceband Data Transmission
IEEE Journal on Selected Areas in Communications
IEEE Transactions on Audio, Speech, and Language Processing
Nonlinear Long-Term Prediction of Speech Based on Truncated Volterra Series
IEEE Transactions on Audio, Speech, and Language Processing
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The main limits on adaptive Volterra filters are their computational complexity in practical implementation and significant performance degradation under the impulsive noise environment. In this paper, a low-complexity pipelined robust M-estimate second-order Volterra (PRMSOV) filter is proposed to reduce the computational burdens of the Volterra filter and enhance the robustness against impulsive noise. The PRMSOV filter consists of a number of extended second-order Volterra (SOV) modules without feedback input cascaded in a chained form. To apply to the modular architecture, the modified normalized least mean M-estimate (NLMM) algorithms are derived to suppress the effect of impulsive noise on the nonlinear and linear combiner subsections, respectively. Since the SOV-NLMM modules in the PRMSOV can operate simultaneously in a pipelined parallelism fashion, they can give a significant improvement of computational efficiency and robustness against impulsive noise. The stability and convergence on nonlinear and linear combiner subsections are also analyzed with the contaminated Gaussian (CG) noise model. Simulations on nonlinear system identification and speech prediction show the proposed PRMSOV filter has better performance than the conventional SOV filter and joint process pipelined SOV (JPPSOV) filter under impulsive noise environment. The initial convergence, steady-state error, robustness and computational complexity are also better than the SOV and JPPSOV filters.