A Novel Method for Nonlinear Processing in Impulsive Noise Based on Gaussianization and Generalized Matching

Zhongtao LUO; Peng LU; Yangyong ZHANG; Gang ZHANG

doi:10.11999/JEIT180191

Volume 40 Issue 12

Nov. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(12): 2928-2935

Zhongtao LUO, Peng LU, Yangyong ZHANG, Gang ZHANG. A Novel Method for Nonlinear Processing in Impulsive Noise Based on Gaussianization and Generalized Matching[J]. Journal of Electronics & Information Technology, 2018, 40(12): 2928-2935. doi: 10.11999/JEIT180191

Citation:

Zhongtao LUO, Peng LU, Yangyong ZHANG, Gang ZHANG. A Novel Method for Nonlinear Processing in Impulsive Noise Based on Gaussianization and Generalized Matching[J]. Journal of Electronics & Information Technology, 2018, 40(12): 2928-2935. doi: 10.11999/JEIT180191

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A Novel Method for Nonlinear Processing in Impulsive Noise Based on Gaussianization and Generalized Matching

doi: 10.11999/JEIT180191

1.
Chongqing Key Laboratory of Signal and Information Processing, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Wuhan Maritime Communication Research Institute, Wuhan 430079, China

Funds: The National Natural Science Foundation of China (61701067, 61771085, 61671095), The Project supported by Scientific Research Foundation of the Chongqing Education Committee (KJ1600427, KJ1600429)

Received Date: 2018-02-11
Rev Recd Date: 2018-07-26

Available Online: 2018-08-03

Publish Date: 2018-12-01

Abstract

Abstract

A method based on Gaussianization and generalized matching, called Gaussianization-Generalized Matching (GGM) method is proposed, for nonlinear processing in impulsive noise. The GGM method can be designed based on noise samples, aided by nonparametric probability density estimation. Thus the GGM design is suitable for nonlinear processing in unknown noise models. The GGM method in the ${\rm S\alpha S}$ model is analyzed, and also the comparison with another approach is presented based on unmatched noise model assumption in the Class A noise. The GGM method is applied to the constant false alarm rate technique via the efficacy function. Simulation and analysis results show that the GGM design is sub-optimal, works robustly when the noise model is unknown, and raises a low requirement on the sample number. Thus, the GGM method provides a promising choice when the noise model is unclear or time-varying.
- Impulsive noise,
- Nonlinear processing,
- Gaussianization,
- Generalized matching,
- Efficiency function

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References(22)

References

KAY S M. Fundamentals of Statistical Signal Processing, Volume II: Detection Theory[M]. Englewood Cliffs, US, Prentice-Hall, Inc., 1993: 94–115.

LACHOS V H, ANGOLINI T, and ABANTO-VALLE C A. On estimation and local influence analysis for measurement errors models under heavy-tailed distributions[J]. Statistical Papers, 2011, 52(3): 567–590 doi: 10.1007/s00362-009-0270-4

DAVIS R R and CLAVIER O. Impulsive noise: A brief review[J]. Elsevier Hearing Research, 2017, 349: 34–36 doi: 10.1016/j.heares.2016.10.020

LI Xutao, JIN Lianwen, and WANG Shouyong. A simplified non-Gaussian mixture model for signal LO detection in -stable interference[C]. IEEE Congress on Image and Signal Processing, Beijing, China, 2008: 403–407.

SHAO M and NIKIAS C L. Signal processing with fractional lower order moments: stable processes and their applications[J]. Proceedings of the IEEE, 1993, 81(7): 986–1010 doi: 10.1109/5.231338

ZHANG Guoyong, WANG Jun, YANG Guosheng, et al. Nonlinear processing for correlation detection in symmetric alpha-stable noise[J]. IEEE Signal Processing Letters, 2018, 25(1): 120–124 doi: 10.1109/LSP.2017.2776317

MIDDLETON D. Procedures for determining the parameters of the first-order canonical models of Class A and Class B electromagnetic interference[J]. IEEE Transactions on Electromagnetic Compatibility, 2007, 21(3): 190–208 doi: 10.1109/TEMC.1979.303731

TSIHRINTZIS G A and NIKIAS C L. Performance of optimum and suboptimum receivers in the presence of impulsive noise modeled as an alpha-stable process[J]. IEEE Transactions on Communications, 1995, 43(2/3/4): 904–914 doi: 10.1109/26.380123

VADALI S R K, RAY P, MULA S, et al. Linear detection of a weak signal in additive Cauchy noise[J]. IEEE Transactions on Communications, 2017, 65(3): 1061–1076 doi: 10.1109/TCOMM.2016.2647599

OH H and NAM H. Design and performance analysis of nonlinearity preprocessors in an impulsive noise environment[J]. IEEE Transactions on Vehicular Technology, 2017, 66(1): 364–376 doi: 10.1109/TVT.2016.2547889

LI Xutao, SUN Jun, WANG Shouyong, et al. Near-optimal detection with constant false alarm ratio in varying impulsive interference[J]. IET Signal Processing, 2013, 7(9): 824–832 doi: 10.1049/iet-spr.2013.0024

LI Xutao, CHEN Peng, FAN Lisheng, et al. Normalization-based receiver using BCGM approximation for -stable noise channels[J]. Electronics Letters, 2013, 49(15): 965–967 doi: 10.1049/el.2013.1289

SWAMI A and SADLER B M. On some detection and estimation problems in heavy-tailed noise[J]. Elsevier Signal Processing, 2002, 82(12): 1829–1846 doi: 10.1016/S0165-1684(02)00314-6

张杨勇, 刘勇. 低频段大气噪声及处理技术[J]. 舰船科学技术, 2008, 30(S1): 85–88 doi: 10.3404/j.issn.1672-7649.2008.S021

ZHANG Yangyong and LIU Yong. Atmospheric-noise at low frequency and its processing technique[J]. Ship Science&Technology, 2008, 30(S1): 85–88 doi: 10.3404/j.issn.1672-7649.2008.S021

WANG Pingbo, LIU Feng, CAI Zhiming, et al. G-Filter's Gaussianization function for interference background[C]. International Conference on Signal Acquisition and Processing, Nanjing, China, 2010: 76–79.

SAMIUDDIN M and EL-SAYYAD G M. On nonparametric kernel density estimates[J]. Biometrika, 1990, 77(4): 865–874 doi: 10.1093/77.4.865

SILVERMAN B W. Density Estimation for Statistics and Data Analysis[M]. London, UK, Chapman & Hall, 1986: 45–48.

HASHEMIFARD Z and AMINDAVAR H. PDF approximations to estimation and detection in time-correlated alpha-stable channels[J]. Elsevier Signal Processing, 2017, 133: 97–106 doi: 10.1016/j.sigpro.2016.10.021

BIBALAN M H, AMINDAVAR H, and AMIRMAZLAGHANI M. Characteristic function based parameter estimation of skewed alpha-stable distribution: An analytical approach[J]. Elsevier Signal Processing, 2017, 130: 323–336 doi: 10.1016/j.sigpro.2016.07.020

KOLODZIEJSKI K R and BETZ J W. Detection of weak random signals in IID non-Gaussian noise[J]. IEEE Transactions on Communications, 2000, 48(2): 222–230 doi: 10.1109/26.823555

ARIF M, NASEEM I, MOINUDDIN M, et al. Design of optimum error nonlinearity for channel estimation in the presence of Class-A impulsive noise[C]. IEEE International Conference on Intelligent and Advanced Systems, Kuala Lumpur, Malaysia, 2017: 1–6.

WEINBERG G V. On the construction of CFAR decision rules via transformations[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(2): 1140–1146 doi: 10.1109/TGRS.2016.2620138

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