The Gaussianization and Generalized Matching Method for Robust Detection in Impulsive Noise

This paper proposes a novel method of nonlinearity design based on Gaussianization and generalized matching (GGM) for signal detection in impulsive noise. Unlike conventional detectors derived from noise models or probability densities, the GGM method is developed based on Gaussianization of noise data and generalized matching of desired signals. Being independent on noise models, the GGM can be designed in a nonparametric way, based on noise samples and via a kernel density estimation method. Analysis and simulations show that the GGM design can be both effective and robust when the noise distribution is unknown.

[1]  Haewoon Nam,et al.  Adaptive Threshold Blanker in an Impulsive Noise Environment , 2014, IEEE Transactions on Electromagnetic Compatibility.

[2]  Daisuke Umehara,et al.  Low rate and high reliable modulation schemes for in-vehicle power line communications , 2011, 2011 IEEE International Symposium on Power Line Communications and Its Applications.

[3]  Jun Sun,et al.  Near-optimal detection with constant false alarm ratio in varying impulsive interference , 2013, IET Signal Process..

[4]  Ananthram Swami,et al.  Non-Gaussian mixture models for detection and estimation in heavy-tailed noise , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[5]  J. McCulloch,et al.  Simple consistent estimators of stable distribution parameters , 1986 .

[6]  Haewoon Nam,et al.  Design and Performance Analysis of Nonlinearity Preprocessors in an Impulsive Noise Environment , 2017, IEEE Transactions on Vehicular Technology.

[7]  S. Zozor,et al.  A Parametric Approach to Suboptimal Signal Detection in $\alpha$-Stable Noise , 2006, IEEE Transactions on Signal Processing.

[8]  H. Vincent Poor,et al.  Parameter estimation for Middleton Class A interference processes , 1989, IEEE Trans. Commun..

[9]  C. D. Kemp,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[10]  Feifei Gao,et al.  Normalisation-based receiver using BCGM approximation for α-stable noise channels , 2013 .

[11]  C. L. Nikias,et al.  Signal processing with alpha-stable distributions and applications , 1995 .

[12]  David Middleton,et al.  Non-Gaussian Noise Models in Signal Processing for Telecommunications: New Methods and Results for Class A and Class B Noise Models , 1999, IEEE Trans. Inf. Theory.

[13]  George A. Wright,et al.  Nonparametric density estimation and detection in impulsive interference channels. I. Estimators , 1994, IEEE Trans. Commun..

[14]  Serena M. Zabin,et al.  Nonparametric Density Estimation and Detection Part I: Estimators ulsive Interference annels - , 1994 .

[15]  Chrysostomos L. Nikias,et al.  Performance of optimum and suboptimum receivers in the presence of impulsive noise modeled as an alpha-stable process , 1995, IEEE Trans. Commun..

[16]  G. Ndo,et al.  Adaptive Noise Mitigation in Impulsive Environment: Application to Power-Line Communications , 2010, IEEE Transactions on Power Delivery.

[17]  Lianwen Jin,et al.  Bi-parameter CGM model for approximation of α-stable PDF , 2008 .

[18]  Brian M. Sadler,et al.  On some detection and estimation problems in heavy-tailed noise , 2002, Signal Process..

[19]  Khaled M. Rabie,et al.  Dynamic Peak-Based Threshold Estimation Method for Mitigating Impulsive Noise in Power-Line Communication Systems , 2013, IEEE Transactions on Power Delivery.

[20]  Sergey V. Zhidkov,et al.  Performance analysis and optimization of OFDM receiver with blanking nonlinearity in impulsive noise environment , 2006, IEEE Transactions on Vehicular Technology.

[21]  A. Spaulding,et al.  Optimum Reception in an Impulsive Interference Environment - Part I: Coherent Detection , 1977, IEEE Transactions on Communications.

[22]  Steven Kay,et al.  Fundamentals Of Statistical Signal Processing , 2001 .

[23]  Kenneth S. Vastola,et al.  Threshold Detection in Narrow-Band Non-Gaussian Noise , 1984, IEEE Trans. Commun..

[24]  Jun Wang,et al.  Nonlinear Processing for Correlation Detection in Symmetric Alpha-Stable Noise , 2018, IEEE Signal Processing Letters.