Aug 22, 2017 the authors of this paper study the synthesis of new models and methods for signal detection in additive correlated non gaussian noise. Signal detection in correlated nongaussian noise using. There have been different statistical distributions proposed to model such impulsive noise such as the. In this suboptimal detection context, a classical approach 2,3 is to implement a non linear scheme composed of a nonlinear preprocessor. Solution for signal detection in nongaussian noise, p roc. Binary signal detection in awgn 1 examples of signal sets. Pdf signal detection in nongaussian noise by a kurtosisbased. Gaussian noise is a particularly important kind of noise because it is very prevalent. Thomas ieee it 1975 gaussian noise shows few outliers impulsive noise is common in practice lightning, glitches, interference, pulses. Pdf signal detection in nongaussian noise by a kurtosis.
Pdf this paper has focused attention on the problem of optimizing signal detection in presence of additive independent stationary nongaussian noise. The probability density function pdf under the two. Recently reported results have confirmed that impulsive noise is present in many indoor 4 and outdoor com munication environments 5 due to a variety of sources. Spaulding new models of electromagnetic interference emi have been developed by middleton 1 l,48,49j over the last decade 19741983, which have pro vided canonical, analytically tractable, and experimentally well estab. Optimum linear detectors, under the assumption of additive gaussian noise are suggested in 1. The paper deals with twosensor interception of cyclostationary signals in the presence of additive non gaussian noise. The obtained detection structure does not depend on the noise univariate probability density function. In case of nongaussian noise, our study shows that rbf signal detector has significant improvement in performance characteristics. Sr, ssr, and parameters affect signal detection for illustration of the possibility of sr and ssr measured by p er and the effect of different noise pdfs on signal detection, we consider the case where. Detection of signals in noise serves as an introduction to the principles and applications of the statistical theory of signal detection. This example demonstrates how a nongaussian distribution can happen in a problem.
The detection of a known deterministic signal in unknown nongaussian noise is a problem of great interest in many fields, such as communications and image processing. On optimal threshold and structure in threshold system based detector. The pdf model is expressed in terms of a fourthorder statistical parameter. The structure of a receiver for detecting binary signals in an awgn channel is shown in figure 2. Finally, in the radar detection scenario, the compoundgaussian model is the most widely accepted and experimental verified. Appendix a detectionandestimationinadditive gaussian noise. Robust signal detection in nongaussian noise using threshold system and bistable system.
Noiseenhanced nonlinear detector to improve signal detection. Robert schober department of electrical and computer engineering university of british columbia vancouver, august 24, 2010. Non gaussian noise cases have received much less attention. For the most part the material developed here can be. Nonlinear signal detection from an array of threshold. Adaptive neural net preprocessing for signal detection in. The authors of this paper study the synthesis of new models and methods for signal detection in additive correlated nongaussian noise. Nongaussian signal detection university of arizona. Of course the focus is on noise which is not gaussian. Radar signal detection in nongaussian noise using rbf neural.
Van trees, detection, estimation and modulation theory part i. Zoubir signal processing group, technische universita. Adaptive bayesian multiuser detection for synchronous cdma. Gaussian noise with performances that should at least overcome the performances of the linear detector and hopefully come as close as possible to the optimal detector performances when the noise is non gaussian.
Desai, which appeared in the proceedings of the fourth international. Nonlinear filtering of nongaussian noise 209 manmade noise sources, such as electronic devices, neon lights, relay switching noise in telephone channels and automatic ignition systems 2, 3. France trading detection of signal in gaussian noise pdf. In this paper, we generate colored gaussian noise, colored non gaussian noise, and non gaussian noise types, these will then be added to singletone sinusoidal signals and fm signals. Signal processing 86 2006 34563465 noiseenhanced nonlinear detector to improve signal detection in nongaussian noise david rousseaua, g. The probability density function of a gaussian random variable is given by. The probability density functions for quantization noise, continuous wave interference, atmospheric noise, and impulse noise are presented and discussed in detail.
Optimum detection and signal design for channels with non. The detector has been tested and applied on an underwater. A neural solution for signal detection in nongaussian noise. Robust distributed sequential hypothesis testing for detecting a random signal in nongaussian noise mark r. As you study it more, youll find that it also has several other important statistical properties.
Signal detection in nongaussian noise is fundamental to design signal processing systems like decision making or information extraction. Detection in nongaussian noise university of washington. Radar signal detection in nongaussian noise using rbf. Purchase detection of signals in noise 2nd edition. Detection of signals in additive white gaussian noise 5. Detectors for discretetime signals in nongaussian noise. Signal detection by generalized detector in compoundgaussian. A new moment quality criterion decision making is proposed based on a random process description using moments and a formation of polynomial decision rules. Detection of binary signal in gaussian noise pdf investing post. Pdf some univariate noise probability density function models. Under analysis, we use the approach discussed in 15.
In this paper, we consider the mai mitigation problem in dscdma channels with nongaussian ambient noise. Signal detection and modulation classification in non. Adaptive neural net preprocessing for signal detection in non. A neural solution for signal detection in nongaussian. For this reason, the main goal of this dissertation is to develop statistical signal processing algorithms for the detection and modulation classi cation of signals in radio channels where the additive noise is non gaussian. Add white gaussian noise to signal matlab awgn mathworks. This is the detection of signals in additive noise which is not required to have gaussian probability density functions in its statistical description. Lets say i have a nongaussian pdf poisson, middleton etc etc. On the detection of a sine wave in gaussian noise author. Entropy, estimation, gaussian noise, gaussian broadcast channel, gaussian wiretap chan. The detection uses the neymanpearson np decision rule to achieve a specified probability of false alarm, pfa. Signal detection by generalized detector in compound.
This detection problem has the following general discretetime. Signal detection and modulation classi cation in non. The book discusses probability and random processes. The authors discuss the need to provide a realistic model of a generic noise probability density function pdf, in order to optimize the signal detection in nongaussian environments. Pdf cyclostationaritybased signal detection and source. In this paper, we generate colored gaussian noise, colored nongaussian noise, and nongaussian noise types, these will then be added to singletone sinusoidal signals and fm signals. However, it requires the knowledge, but for a scale. Pdf radar signal detection in nongaussian noise using. A robust detector of known signal in nongaussian noise using. Nearly optimal detection of signals in nongaussian noise dtic. Kafadar, gaussian whitenoise generation for digital signal synthesis ieee trans on instr and meas, vol. However, the computational complexity of ml detection is quite high, and therefore, effective nearoptimal multiuser detection techniques in nongaussian noise are needed.
Noiseenhanced nonlinear detector to improve signal. It is characterized by a histogram more precisely, a probability density function that follows the bell curve or gaussian function. Gaussian noise is statistical noise having a probability distribution function pdf equal to that of the normal distribution, which is also known as the gaussian distribution. The majority of the signal detection and modulation classification algorithms available in the literature assume that the additive noise has a gaussian distribution. Taking into account parameters of non gaussian distribution of random variables such as the moments of. It may enter the receiver through the antenna along with the desired signal or it may be generated within the receiver. Robust directionofarrival estimation in nongaussian noise core. For example, in watermark detection in discrete cosine transform dct domain, the signal is the watermark or a signature, which is usually known, while the dct coefficients of an image is the noise, whose probability density function pdf is non gaussian and unknown in general. Binary signal detection in awgn 1 examples of signal sets for.
Adding white gaussian noise to a signal hi pulkit, why would we want the noise variance to be equal to 1. Optimum detection and signal design for channels with non but neargaussian additive noise adisai bodharamik, john b. Random signal detection in correlated nongaussian noise. A robust detector of known signal in nongaussian noise. Therefore, to obtain detection structures of easy implementation, some simplifying assumptions about the signal of interest soi and the noise have usually been made. Finally, in the radar detection scenario, the compound gaussian model is the most widely accepted and experimental verified. A class this paper is based on a neural solution for signal detection in nongaussian noise, by d.
Abstractthis paper addresses the problem of sequential. Earlier someone asked about a firm paying 70 pdf winners. Hello everyone, from what i understand, matlabs rand and randn functions generate gaussian noise. Lets say i have a non gaussian pdf poisson, middleton etc etc. Hypotheses test is likelihood value of non gaussian noise used to gravitationalwaves from. We study the performance of the ga detector when operating in the compoundgaussian noise. This paper deals with the problem of finding the optimum method of detecting a sine waave of known frequency and amplitude in the presence of noise. Very widely promoted, but trading a warning list from the sec. Nongaussian impulsive noise has been used to model different noise sources in many communication systems, such as multiple access interference, manmade electromag netic noise, car ignition and mechanical switching and many others. The probability density function of w follows from a. Gaussian noise sensitivity and bosonsampling gil kalaiy guy kindlerz november 11, 2014 abstract we study the sensitivity to noise of jpermanentxj2 for random real and complex n n gaussian matrices x, and show that asymptotically the correlation between the noisy and noiseless outcomes tends to zero when the noise level is. The optimalnearoptimal detector for this problem is the likelihood ratio test lrt or generalized lrt glrt.
Newcomb absrractthe gramcharlier series representation of the noiseprobability density function is used to determine an optimum detector for signals in norrgauaaianbut neargaussian ngng noise. Robust multiuser detection in nongaussian channels. Nonlinear signal detection from an array of threshold devices. This book contains a unified treatment of a class of problems of signal detection theory. Sequence detection in nongaussian noise with hintersymbol.
The contents also form a bridge between the classical results of signal detection in gaussian noise and those of nonparametric and robust signal detection, which are not con sidered in this book. Regazzoni2 department of biophysical and electronic engineering dibe, university of genoa via allopera pia 11a 16145 genova italy phone. Without losing the generality, we assume that the signal power is equal to 1 watt and the noise power is determined accordingly based on the signal to noise ratio snr. For example, in watermark detection in discrete cosine transform dct domain, the signal is the watermark or a signature, which is usually known, while the dct coefficients of an image is. Signal detection in nongaussian noise springerlink. Detection of weak signals in nongaussian noise ning hsing lu on. However, while this is a good model for thermal noise, various studies have shown that the noise experienced in most radio channels, due to a variety of manmade and natural. Here039s how binary options youtube video math works in that example. For information about producing repeatable noise samples, see tips. The vector w w 1 w n t takes values in the vector space n. Synthesize nearly gaussian noise with flat bandlimited white spectrum by means of phase spectrum randomizing in the frequency domain. Receiver noise noise is the unwanted electromagnetic energy that interferes with the ability of the receiver to detect the wanted signal. The locally optimum approach is considered as a starting point to derive cyclostationarityexploiting receiver structures for. We study the performance of the ga detector when operating in the compound gaussian noise.
A matched filter is often used at the receiver front end to enhance. Hypotheses test is likelihood value of nongaussian noise used to gravitationalwaves from. Robust distributed sequential hypothesis testing for. The authors discuss the need to provide a realistic model of a generic noise probability density function pdf, in order to optimize the signal detection in non gaussian environments. Impulsive noise occurs in underwater acoustics and in extremely low frequency communications channels. A standard gaussian random vector w is a collection of nindependent and identically distributed i. Signal detection in non gaussian noise is fundamental to design signal processing systems like decision making or information extraction. In 52, robust multiuser detection methods for impulsive noise cdma channels based on the huber robust regression technique are proposed. Fourth international conference on i nformation technol ogy.
For this reason, the main goal of this dissertation is to develop statistical signal processing algorithms for the detection and modulation classi cation of signals in radio channels where the additive noise is nongaussian. Adaptive neural net preprocessing for signal detection 125 the task explored in this paper is signal detection with impulsive noise where an adaptive nonlinearity is required for optimal performance. For example, in watermark detection in discrete cosine transform dct domain, the signal is the watermark or a signature, which is usually known, while the dct coef. Trading detection of signal in gaussian noise pdf in france. We investigate the nongaussian signal detection in gaussian noise. Impulse noise is described by the hyperbolic and pareto distributions and quantization noise isrepresented by. Frequency estimation of fm signals under nongaussian and. For example, for an snr of 10 db, the noise power, i. In case of non gaussian noise, our study shows that rbf signal detector has significant improvement in performance characteristics. Detection snr threshold for signal in white gaussian noise.
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