However, the computational complexity of ml detection is quite high, and therefore, effective nearoptimal multiuser detection techniques in non gaussian noise are needed. Motivated by the practical and accurate demand of intelligent cognitive radio cr sensor networks, a new modeling method of practical background noise and a novel sensing scheme are presented, where the noise model is the nongaussian colored noise based on. Diversity detection in nongaussian noise employing the. Signal detection and modulation classi cation in nongaussian. However,it requires the knowledge,but for a scale fac. Impulsive noise occurs in underwater acoustics and in extremely low frequency communications channels. This book contains a unified treatment of a class of problems of signal detection theory. A robust and datadependent adaptive thresholding algorithm for nonhomogeneity detection in non gaussian interference is addressed. Joint signal parameter estimation in nongaussian noise by the method of polynomial maximization. Were basically trying to round off every sharp corner in the image which removes individual noise speckles.
Pdf adaptive detection of a gaussian signal in gaussian noise. Radar signal detection in nongaussian noise using rbf. 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. The mathematical limits for noise removal are set by information theory, namely the nyquistshannon sampling theorem. Locally optimal detection of signals in underwater acoustic noise. Solution of the integral equations problems bibliography v. Random signal detection in correlated nongaussian noise core. This is the detection of signals in additive noise which is not required to have gaussian probability density. Radar signal detection in nongaussian noise using rbf neural network. The optimality of the proposed td is proved under the assumptions of white noise, small signal, and a large number of. Estimation of the parameters of sinusoidal signals in nongaussian noise. The paper deals with twosensor interception of cyclostationary signals in the presence of additive non gaussian noise. To the best of our knowledge, there is no previous work on td for detecting an arbitrary signal in nongaussian noise with unknown pdf, which is the focus of this paper. In 101, distributed detection of known signals in correlated non gaussian noise is studied, where.
Detection snr threshold for signal in white gaussian noise. This is a special case of the general mary detection model, described in section 23. Pdf radar signal detection in nongaussian noise using. Signal to noise ratio snr was varied between 0 and 1. Adaptive outlier pursuit is used to detect the outlier and reconstruct the image or signal by iteratively reconstructing the image or signal and adaptively pursuing the outlier. Noiseenhanced nonlinear detector to improve signal. The story the data tells us is often the one wed like to hear, and we usually make sure that it has a happy ending. Nongaussian noise is modeled by gaussian mixture distribution. Signal detection in nongaussian noise, sprin ger verlag, 1988. Unfortunately, conventional signal processing algorithms developed for gaussian noise conditions are known to perform poorly in the presence of non gaussian noise. Training of the neural network for signal detection and its operation at some specified probability of false alarm are discussed.
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. I took the featured image from the top of this article, applied gaussian noise across all 3 color channels, then put it on the above left hand image. Modeling of nongaussian colored noise and application in cr. We employ neural networks to detect known signals in additive nongaussian noise. Schematic of the different detectors for known signal in nongaussian noise. 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.
If the inline pdf is not rendering correctly, you can. Although likelihood ration lr detectors are discussed, primary attention is paid to asymptotic detector performance, and therefore to maximum efficacy or locally optimal lo detectors. Signal detection in nongaussian noise springerlink. This is the detection of signals in addi tive noise which is not required to have gaussian. Typical pdf is buried in nonnecessarily white gaussian the random amplitudes. Modeling of nongaussian colored noise and application in. Also see reference 3 for some recent ternary detection analysis involving purely gaussian noise.
On the problem of optimal signal detection in discrete. Optimum linear detectors, under the assumption of additive gaussian noise are suggested in 1. This detection problem has the following general discretetime. On optimal threshold and structure in threshold system based detector.
In the general framework of radar detection, estimation of the gaussian or nongaussian clutter covariance. Gaussian noise detection and removal in imaging wirebiters. Adaptive neural net preprocessing for signal detection in non. Vincent poor, fellow, ieee abstract in many wireless systems where multiuser detection techniques may be applied, the ambient channel noise is known through experimental measurements to be decidedly nongaussian, due largely to impulsive phenomena. Receiver noise noise is the unwanted electromagnetic energy that interferes with the ability of the receiver to detect the wanted signal. Robust signal detection in non gaussian noise using threshold system and bistable system. The design of a locally optimal detector for a known signal in nongaussian noise is discussed. Adaptive detection of a gaussian signal in gaussian noise. Pdf radar signal detection in nongaussian noise using rbf. Gaussian noise preferred over gaussian noise in signal. A robust and datadependent adaptive thresholding algorithm for nonhomogeneity detection in nongaussian interference is addressed. Random signal detection in correlated nongaussian noise mario tanda.
Signal detection and modulation classification in nongaussian noise. Citeseerx document details isaac councill, lee giles, pradeep teregowda. In the context of digital signal processing addressed to underwater acoustic communications, this work focuses attention on the optimization of detection of weak signals in presence of additive independent stationary non gaussian noise. Saleem a kassam this book contains a unified treatment of a class of problems of signal detection theory. An asymptotically optimum structure for the detection of a gaussian signal is synthesized. Using a new concept of moment quality criterion and new methods, we have developed nonlinear algorithms, computer tools and a new strategy for addressing the problem of signal detection in correlated nongaussian noise. The work concentrates on noise sources whose distributions fail to satisfy some commonly held assumptions. Pdf signal detection in nongaussian noise by a kurtosisbased. Nov 16, 2017 motivated by the practical and accurate demand of intelligent cognitive radio cr sensor networks, a new modeling method of practical background noise and a novel sensing scheme are presented, where the noise model is the nongaussian colored noise based on. A robust detector of known signal in nongaussian noise using. Every column of the matrix is calculated into mlevel decomposition. 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. The optimal detector nonlinearity is approximated adaptively in the noise pdf tail region, and a polynomial is used to approximate the nonlinearity near the mean.
The problem of hosbased signal detection methods applied in real communication systems is addressed. A neural solution for signal detection in nongaussian noise. 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. Anomaly detection and removal using non stationary gaussian processes steven reece. Although kalman filter versions that deal with nongaussian noise processes exist, the noise. Joint signal parameter estimation in nongaussian noise by.
The algorithm is to be used as a preprocessing technique to select a set of homogeneous data from a bulk of nonhomogeneous compound gaussian secondary data employed for adaptive radar. Robust multiuser detection in nongaussian channels signal. Signal detection in correlated nongaussian noise using. The algorithm is to be used as a preprocessing technique to select a set of homogeneous data from a bulk of nonhomogeneous compoundgaussian secondary data employed for adaptive radar. Robert schober department of electrical and computer engineering university of british columbia vancouver, august 24, 2010.
Signal detection in nongaussian noise by a kurtosisbased probability density function model. Detection in nongaussian noise university of washington. The problem of detecting the presence of a random signal embedded in additive correlated non gaussian noise modeled as a spherically invariant random process is addressed. Signal detection in nongaussian noise 1988 edition. A class this paper is based on a neural solution for signal detection in nongaussian noise, by d. Image and signal processing with nongaussian noise. Signal detection and modulation classi cation in non. Detection of binary signal in gaussian noise pdf investing post. Fourth international conference on i nformation technol ogy. Radar signal detection in nongaussian noise using rbf neural. 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. And yet if the tragedy of julius caesar turned on an ancient idea of prediction associating it with fatalism, fortunetelling, and superstitionit also introduced a more. It may enter the receiver through the antenna along with the desired signal or it may be generated within the receiver.
Performance of neural detectors are presented under several nongaussian noise environments and are compared with those of matched filter and locally. For this reason, the main goal of this dissertation is to develop statistical signal processing algorithms for the detection and modulation classification of signals in radio. Model of initial ligo design of signaltonoise only the random. Three canonical problems of signal detection in additive noise are covered here. Of course the focus is on noise which is not gaussian.
This thesis provides two classes of algorithms for dealing with some special types of non gaussian noise. Reiterative robust adaptive thresholding for nonhomogeneity. Unfortunately, conventional signal processing algorithms developed for gaussian noise. 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 non gaussian. Although likelihood ration lr detectors are discussed, primary attention is paid to asymptotic detector performance, and therefore to maximum efficacy or. Add white gaussian noise to signal matlab awgn mathworks. In this paper, we take locally optimum approach to develop weak transient signal detection utilizing laguerre recurrent networks for subspace projection, as well as radial basis function networks for tracking non. Transient signal detection in nongaussian noise using.
Pdf cyclostationaritybased signal detection and source. The detection uses the neymanpearson np decision rule to achieve a specified probability of false alarm, pfa. One may then ask if knowledge of the univariate statistics and the covariance function of a nongaussian process is sufficient or even reasonable for solving the problem of optimum signal detection in this nongaussian noise. Solution for signal detection in nongaussian noise, p roc. Nllength samples of signal are arranged into a matrix. A new hosbased model for signal detection in nongaussian. In this paper, we consider the mai mitigation problem in dscdma channels with non gaussian ambient noise. The detection of weak transient signal buried in nongaussian noise is investigated. Noise reduction, the recovery of the original signal from the noisecorrupted one, is a very common goal in the design of signal processing systems, especially filters.
Random signal detection in correlated nongaussian noise. Signal detection is imperative in underwater signal processing and digital. The performance of these linear and nonlinear detectors have been compared in a bayesian and in a neymanpearson detection strategy when the signal to be detected and the native nongaussian noise are known a priori. Pdf comparison of methodologies for signal detection in. To illustrate the structure and performance of these nonlinear detectors for a wide range of nongaussian noise. Regazzoni2 department of biophysical and electronic engineering dibe, university of genoa. Pdf this paper has focused attention on the problem of optimizing signal detection in presence of additive. Signal detection and modulation classification in non. Radar signal detection in nongaussian noise using rbf neural network article pdf available in journal of computers 31 august 2008 with 308 reads how we measure reads. This situation is frequently encountered in radar, sonar and communication applications. Statistical theory of signal detection 2nd edition. We assume that only one fault occurs at any one time and model the signal by two separate non parametric gaussian process models for both the physical phenomenon. Detectors for discretetime signals in nongaussian noise ieee.
Aug 22, 2017 using a new concept of moment quality criterion and new methods, we have developed non linear algorithms, computer tools and a new strategy for addressing the problem of signal detection in correlated non gaussian noise. We employ this rbf neural detector to detect the presence or absence of a known signal corrupted by different gaussian and nongaussian noise components. Noiseenhanced nonlinear detector to improve signal detection. It is shown that for a noise density with a slower rate of decay in the tails than the gaussian distribution that a non. 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. Asymptotic performance with gaussian noise when the number of sensors goes to infinity is examined. Nongaussian noise an overview sciencedirect topics. For the most part the material developed here can be.
Generalized detector, constant false alarm rate, detection performance, gaussian noise, radar. Threshold detection in correlated nongaussian noise fields ieee. Regazzoni2 department of biophysical and electronic engineering dibe, university of genoa via allopera pia 11a 16145 genova italy phone. In addition, we have developed a new generator of correlated non gaussian processes to carry out simulation.
Robust multiuser detection in nongaussian channels xiaodong wang, member, ieee, and h. Gaussian noise environment results in higher detection probabilities. Threshold detection in correlated nongaussian noise fields. This comparison is meaningful since the linear detectors are often used even when the noise is a priori known to be nongaussian. This report deals with the problems of detecting a known signal in nongaussian or dependent noise. In the other model, correlation coefficient between any two sensors is a constant. This example discusses the detection of a deterministic signal in complex, white, gaussian noise. Signal detection and modulation classification in nongaussian. The detector has been tested and applied on an underwater. In addition, we have developed a new generator of correlated nongaussian processes to carry out simulation. It can be applied either under the ideal but often not realistic assumption of gaussian background. In 101, distributed detection of known signals in correlated nongaussian noise is studied, where.
Signal, noise, and detection limits in mass spectrometry. Unfortunately, conventional signal processing algorithms developed for gaussian noise conditions are known to perform poorly in the presence of nongaussian noise. Recall that the probability density function pdf of the normal or gaussian distribution is. The problem of detecting the presence of a random signal embedded in additive correlated nongaussian noise modeled as a spherically invariant random process is. Search for library items search for lists search for contacts search for a library. The performance of the linear glrt detector, which is optimal under the gaussian background noise assumption, would be badly degraded. A robust detector of known signal in nongaussian noise. The locally optimum lo criterion is selected from a large number of detection criteria. In particular, the interest here is in nonstationary, nongaussian environments. Discriminating secondary from primary nongaussian signals. In this paper, we suggest a neural network signal detector using radial basis function network for detecting a known signal in presence of gaussian and nongaussian noise.
The pdf model is expressed in terms of a fourthorder statistical parameter. Fault removal does not delete the faulty signal data but, instead, massages the fault from the data. The locally optimum approach is considered as a starting point to derive cyclostationarityexploiting receiver structures for. This will provide despeckling but also reduce image detail around sharp corners. In this paper, we consider the problem of mary signal detection based on the generalized approach to signal processing gasp in noise over a singleinput multipleoutput simo channel affected by frequencydispersive rayleigh distributed fading and corrupted by additive nongaussian noise modeled as spherically invariant random process. Signal detection in non gaussian noise is fundamental to design signal processing systems like decision making or information extraction. An iterative version of the algorithm is also suggested in situations of. Desai, which appeared in the proceedings of the fourth international. Robust multiuser detection in nongaussian channels. Expressions exist for density functions pdf binary. Emphasis is on the analysis and synthesis of different methods of detection when the noise distributions are not completely known.
The generalized gaussian class of noise densities is considered relative to detection probabilities for a recently proposed locally optimum discrete. Pdf signal detection is important in sonar and underwater digital communication. Weak transient signal detection in nongaussian noise using. Pdf signal detection in nongaussian noise by a kurtosis. Obtaining high quality images is very important in many areas of applied sciences, and the first part of this thesis is on expectation maximization emtype algorithms for image reconstruction with poisson noise and weighted gaussian noise. Signal detection by generalized detector in compound. Neural networks for signal detection in nongaussian noise. Weak transient signal detection in nongaussian noise. Signal processing 86 2006 34563465 noiseenhanced nonlinear detector to improve signal detection in nongaussian noise david rousseaua, g. Signal, noise, and detection limits in mass spectrometry technical note abstract in the past, the signaltonoise of a chromatographic peak determined from a single measurement has served as a convenient figure of merit used to compare the performance of two different ms systems. Thomas ieee it 1975 gaussian noise shows few outliers impulsive noise is common in practice lightning, glitches, interference, pulses. In this paper, we consider the problem of mary signal detection based on the generalized approach to signal processing gasp in noise over a singleinput multipleoutput simo channel affected by frequencydispersive rayleigh distributed fading and corrupted by additive non gaussian noise modeled as spherically invariant random process. This report deals with the problems of detecting a known signal in non gaussian or dependent noise.
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