Adaptive Threshold Blanker in an Impulsive Noise Environment

Detection of known signals embedded in additive noise is a fundamental problem in signal processing. For normally distributed noise, it is well known that the popular matched filter detector (MFD) is optimal. However, for impulsive noise whose distribution has a heavier tail, the performance of MFD will deteriorate severely. To deal with such problem, in this paper, we propose a novel detector, termed as Gini correlator (GC), and derive the analytic forms of its expectation and variance, under a specified contaminated Gaussian model (CGM) emulating a frequently encountered scenario in practice. In order to further understand its properties, we compare the proposed GC with five state-of-the-art detectors permeating in the literature, in terms of Pitman asymptotic relative efficiency (ARE), as well as time-delay estimation. Monte Carlo simulations not only validate our theoretical discoveries, but also demonstrate the advantages of GC in the aspects of 1) accurate control of false alarm probability without prior knowledge of noise distribution, 2) comparable performance with MFD for Gaussian noise, 3) better performances over the classical detectors in the aspects of greater ARE and smaller bias and standard deviation for time-delay estimation. The theoretical and empirical findings in this work enable GC to be a useful alternative to the existing detectors whether or not impulsivity exists in noise. INDEX TERMS Gini correlator (GC), Kendall's tau (KT), locally optimal detector (LOD), Pitman asymptotic relative efficiency (ARE), sign correlator (SC), Spearman's rho (SR).

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“…Substituting (109) and (121) into (15) and tidying up, we finally arrive at (33), thus completing the proof.…”

Section: Appendix F Proof Of Theoremmentioning

confidence: 62%

Performance Analysis of Gini Correlator for Detecting Known Signals in Impulsive Noise

Chen

Dai

et al. 2019

IEEE Access

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“…The noise samples

are assumed to be additive white noise and their distribution is either Gaussian noise or impulsive noise. Among various impulsive noise models, Middleton class A noise and S

S noise models are widely accepted in the literature because their models are derived from the real physical mechanisms that generate disturbance in communication systems and agree well with physical phenomena [ 1 , 2 ].…”

Section: Noise Models and Motivation For Noise Classificationmentioning

confidence: 99%

Design of a Test for Detecting the Presence of Impulsive Noise

Seo

Nam

2020

Sensors

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This paper proposes a new test method of detecting the presence of impulsive noise based on a complementary cumulative density function (CCDF). Impulsive noise severely degrades performance of communication systems and the conventional Kolmogorov–Smirnov (K–S) test may not perform well, because the test does not consider the characteristics of impulsive noise. In order to detect the presence of impulsive noise reliably, the CCDF of measurement samples is analyzed and compared with the CCDF of additive white Gaussian noise to find the difference between those CCDFs. Due to the nature of heavy-tails in impulsive noise, only the maximum difference may not be sufficient for the accurate detection of impulsive noise. Therefore, the proposed method applies the test hypothesis using the weighted sum of all the differences between those CCDFs. Simulation results justify that the proposed test is more robust and provides lower miss detection probability than the K–S test in the presence of impulsive noise.

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“…The major advantage of this optimal receiver based on thresholding, was that, it was very well suited to wireless sensor networks, and could be easily implemented in software, as well as in hardware. The majority of optimal receivers implemented to eliminate the effects of impulsive noise were based on thresholding the amplitudes at the receiver input [20][21][22]. This technique was used to improve the robustness of our communication system, to face the industrial noise.…”

Section: Performancesmentioning

confidence: 99%

Discrete Wavelet Packet Transform-Based Industrial Digital Wireless Communication Systems

et al. 2019

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The industrial internet of things (IIoT) known as industry 4.0, is the use of internet of things technologies, via the Wireless Sensor Network (WSN), to enhance manufacturing and industrial processes. It incorporates machine learning and big data technologies, to allow machine-to-machine communication that have existed for years in the industrial world. Therefore, it is necessary to propose a robust and functional communication architecture that is based on WSNs, inside factories, in order to show the great interest in the connectivity of things in the industrial environment. In such environment, propagation differs from other conventional indoor mediums, in its large dimensions, and the nature of objects and obstacles inside. Thus, the industrial medium is modeled as a fading channel affected by an impulsive and Gaussian noise. The objective of this paper is to improve robustness and performances of multi-user WSN architecture, based on Discrete Wavelet Transform, under an industrial environment using conventional channel coding and an optimal thresholding receiver.

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Adaptive Threshold Blanker in an Impulsive Noise Environment

Cited by 20 publications

References 17 publications

Performance Analysis of Gini Correlator for Detecting Known Signals in Impulsive Noise

Performance Analysis of Gini Correlator for Detecting Known Signals in Impulsive Noise

Design of a Test for Detecting the Presence of Impulsive Noise

Discrete Wavelet Packet Transform-Based Industrial Digital Wireless Communication Systems

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