We study the performance of the ga detector when operating in the compoundgaussian noise. Robert schober department of electrical and computer engineering university of british columbia vancouver, august 24, 2010. Signal detection and modulation classi cation in non. The performance is comparable to lo detector and superior to the matched filter. The authors of this paper study the synthesis of new models and methods for signal detection in additive correlated nongaussian noise. 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.
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. Neural networks for signal detection in nongaussian noise ieee. In the first part, the problem of signal detection in fractional gaussian noise is considered. A matched filter is often used at the receiver front end to enhance. In 101, distributed detection of known signals in correlated nongaussian noise is studied, where the noise is restricted to be circularly symmetric. 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. Gaussian noise, named after carl friedrich gauss, is statistical noise having a probability density function pdf equal to that of the normal distribution, which is also known as the gaussian distribution. Adaptive bayesian multiuser detection for synchronous cdma. In this paper, we propose a thresholdsystembased detector td for detecting a known deterministic signal in independent nongaussian noise whose probability density function pdf is unknown but is symmetric and unimodal. Based on these noise models, this thesis presents a complete analysis of several estimation schemes. 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. For many nongaussian noise distributions such as double exponential, contaminated gaussian, cauchy and impulsive noise components. Highlights the proposed threshold detector can detect any known signal in non gaussian noise.
Hello everyone, from what i understand, matlabs rand and randn functions generate gaussian noise. Pdf signal detection in nongaussian noise by a kurtosis. The optimality of the proposed threshold detector td is proved. Fast noise level estimation from a single image degraded with. On the detection of a sine wave in gaussian noise author. Signal processing 86 2006 34563465 noiseenhanced nonlinear detector to improve signal detection in nongaussian noise david rousseaua, g. Detection and estimation of chirp signals in nongaussian.
Signal detection and modulation classi cation in nongaussian. 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. Several strategies may be employed to handle nongaussianity. As you study it more, youll find that it also has several other. In this suboptimal detection context, a classical approach 2,3 is to implement a non linear scheme composed of a nonlinear preprocessor followed by the linear scheme that would be used in a gaussian noise. Taking into account parameters of nongaussian distribution of random variables such. Correlated noise and whitening xn sn x prewhitening. However, the transformation works only for a limited range for the skewness and kurtosis see 2 for more details. This book contains a unified treatment of a class of problems of signal detection theory. The robustness of the proposed td is analyzed in details.
In case of nongaussian noise, our study shows that rbf signal detector has significant improvement in performance characteristics. 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. In other words, the values that the noise can take on are gaussian distributed. In this paper, we consider the mai mitigation problem in dscdma channels with nongaussian ambient noise. Impulsive noise is another common nongaussian noise source that is common in fmri time series.
Derivation of the correction factors the four factors are calculated experimentally by using 21 images in fig. Gaussianbased sensor pattern noise extraction as the unique characteristics of imaging sensors, photo response nonuniformity based on gaussian filter is extracted which is called as gaussianbased sensor pattern noise gspn. Detection snr threshold for signal in white gaussian noise. Finally, in the radar detection scenario, the compound gaussian model is the most widely accepted and experimental verified. The problem of detecting the presence of a random signal embedded in additive correlated nongaussian noise modeled as a spherically invariant random process is. Entropy, estimation, gaussian noise, gaussian broadcast channel, gaussian wiretap chan. 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.
The paper deals with twosensor interception of cyclostationary signals in the presence of additive nongaussian noise. Over 10 million scientific documents at your fingertips. In such an environment, the filters designed for gaussian noise are susceptible to poor performance and even divergence. Signal detection in correlated nongaussian noise using. There have been different statistical distributions proposed to model such impulsive noise such as the. 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. Radar signal detection in nongaussian noise using rbf. Kafadar, gaussian whitenoise generation for digital signal synthesis ieee trans on instr and meas, vol. Very widely promoted, but trading a warning list from the sec. Receiver noise noise is the unwanted electromagnetic energy that interferes with the ability of the receiver to detect the wanted signal.
The majority of the signal detection and modulation classification algorithms available in the literature assume that the additive noise has a gaussian distribution. Adaptive neural net preprocessing for signal detection in. Appendix a detectionandestimationinadditive gaussian noise. Detection of signals in additive white gaussian noise 5. For noncoherent samples in white gaussian noise, detection of a nonzero mean leads to a squarelaw detector. For the relationships between snr and other measures of the relative power of the noise, such as e s n 0, and e b n 0, see awgn channel noise level. Acoustic impulsive noise based on nongaussian models. 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. Trading detection of signal in gaussian noise pdf in france.
Binary signal detection in awgn 1 examples of signal sets for. We employ neural networks to detect known signals in additive non gaussian noise. Taking into account parameters of non gaussian distribution of random variables such as the moments of. Impulsive noise occurs in underwater acoustics and in extremely low. 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 detection and modulation classification in non. Of course the focus is on noise which is not gaussian.
Non gaussian noise cases have received much less attention. A robust detector of known signal in nongaussian 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. A robust detector of known signal in nongaussian noise using. Detection of signal discontinuities from noisy fourier data. Robust multiuser detection in nongaussian channels.
The linear detection threshold for the noncoherent np detector is. The locally optimum approach is considered as a starting point to derive cyclostationarityexploiting receiver structures for. Squares, maximum likelihood and maximum a posteriori estimators ashish raj, phd. A neural solution for signal detection in nongaussian. Distributed detection of a signal in generalized gaussian noise. Nonlinear signal detection from an array of threshold devices. The probability density function of w follows from a. 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 nongaussianity is introduced by a target skewness and a target kurtosis.
Additive because it is added to any noise that might be intrinsic to the information system. Nonlinear bayesian estimation of bold signal under non. The structure of a receiver for detecting binary signals in an awgn channel is shown in figure 2. However, the computational complexity of ml detection is quite high, and therefore, effective nearoptimal multiuser detection techniques in nongaussian noise are needed. Therefore, to obtain detection structures of easy implementation, some simplifying assumptions about the signal of interest soi and the noise have usually been made. Threshold detection in correlated nongaussian noise fields ieee. For the former case, the noise is assumed to have a gaussian distribution, i. After that, parameter estimation for the class a model was carried out with the. The vector w w 1 w n t takes values in the vector space n. Nongaussian process generation file exchange matlab central. Nongaussian noise an overview sciencedirect topics. Nonlinear signal detection from an array of threshold.
Noiseenhanced nonlinear detector to improve signal. Pdf cyclostationaritybased signal detection and source. Impulsive noise occurs in underwater acoustics and in extremely low frequency communications channels. For example, for an snr of 10 db, the noise power, i. Frequency estimation of fm signals under nongaussian and. Gaussian noise is a particularly important kind of noise because it is very prevalent. Binary signal detection in awgn 1 examples of signal sets. Signal detection in nongaussian noise springerlink. Pdf signal detection in nongaussian noise by a kurtosisbased.
Adaptive neural net preprocessing for signal detection in non. To facilitate the study of this problem, several results related to the reproducing kernel hilbert space of fractional brownian motion are presented. These processes encompass a large number of nongaussian distributions mentioned previously and include, of course, gaussian. Lets say i have a nongaussian pdf poisson, middleton etc etc.
Detection of binary signal in gaussian noise pdf investing post. 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. One of the most general and elegant nongaussian noise model is provided by the compoundgaussian process which includes the socalled spherically invariant random vectors sirvs. A class this paper is based on a neural solution for signal detection in nongaussian noise, by d. We study the performance of the ga detector when operating in the compound gaussian noise. Pdf this paper has focused attention on the problem of optimizing signal detection in presence of additive independent stationary nongaussian noise. Earlier someone asked about a firm paying 70 pdf winners. A new moment quality criterion decision making is proposed based on a random process description using moments and a formation of polynomial decision rules. Radar signal detection in nongaussian noise using rbf neural. This detection problem has the following general discretetime. 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.
France trading detection of signal in gaussian noise pdf. Threshold detection in correlated nongaussian noise fields. Pdf comparison of methodologies for signal detection in. It is characterized by a histogram more precisely, a probability density function that follows the bell curve or gaussian function.
The intensity i of a frame can be modeled as follows. Here039s how binary options youtube video math works in that example. The important special case of this treated here is the detection of phaseincoherent narrowband signals in narrowband noise. Pdf detection of random signals in gaussian mixture noise. We found that rbf network signal detector performance is very close to that of mf and bp detector for gaussian noise. The pdf of the class b noise does not have a single closed form. Under analysis, we use the approach discussed in 15. Finally, in the radar detection scenario, the compoundgaussian model is the most widely accepted and experimental verified. Jul 19, 2015 the nongaussianity is introduced by a target skewness and a target kurtosis. It may enter the receiver through the antenna along with the desired signal or it may be generated within the receiver. Signal detection by generalized detector in compoundgaussian.
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. Hypotheses test is likelihood value of nongaussian noise used to gravitationalwaves from. Training of the neural network for signal detection and its. Signal detection and modulation classification in nongaussian. The optimality of the proposed td is proved under the assumptions of white noise, small signal, and a large number of samples. Detection in nongaussian noise university of washington. The probability density function of a gaussian random variable is given by. Add white gaussian noise to signal matlab awgn mathworks. Signal detection in fractional gaussian noise and an rkhs. The probability density functions for quantization noise, continuous wave interference, atmospheric noise, and impulse noise are presented and discussed in detail. The detection uses the neymanpearson np decision rule to achieve a specified probability of false alarm, pfa. Robust multiuser detection in nongaussian channels signal.
Optimum linear detectors, under the assumption of additive gaussian noise are suggested in 1. 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. 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. Pdf some univariate noise probability density function models. Additive white gaussian noise awgn is a basic noise model used in information theory to mimic the effect of many random processes that occur in nature. Spanning the gap between the known and the random signal detection problems is that of detection of a deterministic signal with random parameters in noise. 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. We employ neural networks to detect known signals in additive nongaussian noise. An example is middleton classes a, b, and c models 25, 26. Nongaussian process generation file exchange matlab.
Therefore, the maximum likelihood decorrelating detector in nongaussian noise with pdf. Using matched filter as benchmark, the validity region is defined and analyzed. Hypotheses test is likelihood value of non gaussian noise used to gravitationalwaves from. This model of noise is sometimes referred to as additive white gaussian noise or awgn. Noiseenhanced nonlinear detector to improve signal detection. Synthesize nearly gaussian noise with flat bandlimited white spectrum by means of phase spectrum randomizing in the frequency domain. Then, we show the probability density function pdf fitting for gaussian. A middleton class a noise model considering impulsive noise was used to model nongaussian noise in shallow waters. 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. Furthermore, the authors in 102 examine twosensor distributed detection of known signals in correlated t distributed noise. Sequence detection in nongaussian noise with hintersymbol. Random signal detection in correlated nongaussian noise. A standard gaussian random vector w is a collection of nindependent and identically distributed i.
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