Byrne department of mathematical sciences university of massachusetts lowell lowell, ma 01854. Some univariate noise probability density function models. Conventional signal processing algorithms, based on. Incidentally, as the noise model is required to be more accurate, the ease of analysis as that of a gaussian pdf disappears. Reliable parameter estimation for generalised gaussian pdf. To this end, the best nongaussian noise model that has been presented in specialized literature is the huber estimator in the article by c. Robust multiuser detection in nongaussian channels signal.
The term is used, with this or similar meanings, in many scientific and technical disciplines, including physics, acoustical engineering, telecommunications, and statistical forecasting. Kwak and ha 2004 described the use of the grinding force signal with noise reduction to detect the dressing time based on dwt. 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. In signal processing, white noise is a random signal having equal intensity at different frequencies, giving it a constant power spectral density. Schaubt, additional resources using a robust estimator algorithm, which is able to handle multipath gnss signals as well as intentional and unintentional interferences. Desai, which appeared in the proceedings of the fourth international. A new hosbased model for signal detection in nongaussian. Wavelet denoising has been employed in tcm in some studies. High requirements for energy efficiency on the one hand and a low information transfer rate allows the use of signals with a small spectrum width, including flicker noise spectral regions. Signal processing operations such as a dft, tend to manifest central limit theorem effects on data, so you need to be aware that even linear transformations will not preserve a non gaussian pdf. The most widely used model is the gaussian random process. In section iii, we propose and analyze a robust technique for multiuser detection is nongaussian channels, which is essentially a. As a result of denoising, the grinding force signal was successfully used to detect the need for dressing.
In another book, it reads often, an image is considered to be the realization of a spatial stochastic process 704. In section ii, the signal model for a dscdma communication system, as well as the impulsive channel noise model, is described. Gaussian pdf, the middleton class a pdf, and some such pdfs are employed to model nongaussian noise 4. Recall that the probability density function pdf of the normal or gaussian distribution is.
The signal to noise ratio snr was 10db and the four nongaussian signals were of equal strength. The design of a locally optimal detector for a known signal in nongaussian noise is discussed. Image reconstruction under nongaussian noise dtu orbit. Attempts have been made to model the bold signal by a set of nonlinear, nonautonomous differential equations linking hemodynamic changes with a set of psychological variables. Two pdf models suitable for describing nongaussian iid noise are introduced. Random signal detection in correlated nongaussian noise.
It may enter the receiver through the antenna along with the desired signal or it may be generated within the receiver. White noise refers to a statistical model for signals and. Mary signal detection based on the generalized approach to signal processing gasp in noise over a singleinput multipleoutput simo channel affected by the frequencydispersive rayleigh distributed fading and corrupted by the additive nongaussian noise modelled as spherically invariant random process. It was desired to find a useful class of noise distributions ut is a process slowly varying compared to zt and. In this paper, we present a third approach to deal with a nongaussian noise environment, by employing the polynomial. Robert schober department of electrical and computer engineering university of british columbia vancouver, august 24, 2010. An important requirement for most signal processing problems is the speci. Of course the focus is on noise which is not gaussian. The detector has been tested and applied on an underwater acoustics experiment. In applications, however, the gaussian pdf is much more widely used.
A robust detector of known signal in nongaussian noise. Receiver noise noise is the unwanted electromagnetic energy that interferes with the ability of the receiver to detect the wanted signal. Nongaussian noise spectroscopy with a superconducting. Frequency estimation of fm signals under nongaussian and.
Sanjeev arulampalam, simon maskell, neil gordon, and tim clapp abstract increasingly, for many application areas, it is becoming important to include elements of nonlinearity and. Thus, the adoption of suitable models must be considered to reach accuracy and acceptable performance for these solutions. Hosbased noise models for signaldetection optimization. In particular, class a noise describes the type of electromagnetic interference emi often encountered in telecommunication applications, where this. An analysis of transient impulsive noise in a poisson. Mathematical models of correlated nongaussian processes using. This thesis provides two classes of algorithms for dealing with some special types of nongaussian noise.
Signal detection in correlated nongaussian noise using higher. 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 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. The following example illustrates a problem in which this can happen. Image and signal processing with nongaussian noise. Wim van drongelen, in signal processing for neuroscientists second. Analytic alphastable noise modeling in a poisson field of.
Nongaussian noise an overview sciencedirect topics. To examine this issue, we present two general models both previously described in the literature for nongaussian noise and show that they may be used to construct two different nongaussian. In addition to these natural nongaussian noise sources, there is a great variety of manmade nongaussian noise sources such as automobile ignitions, neon lights, and other electronic devices 6li. An example nongaussian distribution for a state variable. Denoising is a common practical problem in signal processing. Therefore, it is of great importance to address this problem in 3dmimo channel estimation. First, we establish the nongaussian colored noise model through combining. Radar signal detection in nongaussian noise using rbf. Five instances of the posterior are plotted by thin blue lines and. Results obtained in the context of an underwater acoustic application are encouraging. Although there are some studies on more realistic noise model with nongaussian distributions, few signal processing solutions have been established compared to those with gaussian assumption. Modeling of nongaussian colored noise and application in.
Before introducing our experimental test bed, we present the general setting to which our analysis is relevant. A class this paper is based on a neural solution for signal detection in nongaussian noise, by d. From the viewpoint of this model, the underlying nongaussian process consists of a series of gaussian components, with different probability weight factors and. This is partly because the noise pdf has a nonzero centre.
All signal processing techniques exploit signal structure. Statistical analysis and a new penalty term boaz nadler and leonid aryeh kontorovich abstractdetection of the number of sinusoids embedded in noise is a fundamental problem in statistical signal processing. Especially for signal processing methods that rely on secondorder statistics 1, the gaussian assumption of. As a result, it allows the exploitation of the parametric generalised gaussian pdf model in advanced signal processing applications, e. Signal processing in nongaussian noise using mixture. Acoustic impulsive noise based on nongaussian models mdpi. Modeling of nongaussian colored noise and application in cr multi. Moreover, a performance comparison with the locally optimum detector synthesized. One of the fundamental tasks of image processing is to reconstruct the clean image. Impulse noise is described by the hyperbolic and pareto distributions and quantization noise. Signal detection in correlated nongaussian noise using. Nonlinear bayesian estimation of bold signal under non.
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. Density function pdf fitting for gaussian, gmm with two gaussians and s. The reason for the nongaussian noise may be, for example, an atmospheric noise in radio links, lightning, relay contacts, ambient acoustic noise due to ice cracking in the arctic region in underwater sonar and submarine communications 10,11. 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. In the study of nongaussian noise models in signal processing, middleton proposed the gaussian mixture model 16. Various attempts have been made to develop models of nongaussian noises. An approach for nongaussian signal processing is presented in this paper that is based on modeling the probability density function pdf of the additive noise with a finite mixture of gaussian pdfs. Polynomial transformation method for nongaussian noise.
With gaussian noise 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. Nongaussian signal an overview sciencedirect topics. Although kalman filter versions that deal with nongaussian noise processes. The probability density functions for quantization noise, continuous wave interference, atmospheric noise, and impulse noise are presented and discussed in detail. Signal detection in nongaussian noise springerlink. Such signals can be either be bothersome noise or informationbearing discharges of. It should be also noted that from a practical perspective, gaussianity isnt black and white. In this paper, the primary interest is to provide a general model for wireless channel in presence of these transient impulsive noise for spacetime signal processing problems. So the signal to noise ratio for nongaussian colored noise is defined by. The efficiency of the adapted 57 and classic 60 dr is shown in fig. A tutorial on particle filters for online nonlinearnon. Nongaussian noise models in signal processing for telecommunications. Model parameters are estimated using iterative procedures derived from the.
Citeseerx document details isaac councill, lee giles, pradeep teregowda. Attention is focused primarily on the authors canonical. The power spectral density of the novel model is shown in fig. Attention is focused primarily on the authors canonical statisticalphysical class a and class b models. Optimum linear detectors, under the assumption of additive gaussian noise are suggested in 1. Diversity detection in nongaussian noise over fading.
Random signal detection in correlated nongaussian noise mario tanda. A number of models have been proposed for nongaussian phenomena, either by. The pdf model is expressed in terms of a fourthorder statistical parameter. Abstractin substations, the presence of random transient impulsive interference sources makes noise highly nongaussian. However, in some environments, the gaussian noise model may not be appropriate 1. S distribution has only the fractional lower moments, and its variance does not exist, the conventional signal to noise ratio is meaningless. Using adequate mathematical models of random processes and methods of signal processing allows us to improve the efficiency of signal detection in correlated nongaussian noise. Robust multiuser detection in nongaussian channels. The models are used in the design of a lod test for detecting weak signals in real nongaussian noise. Pdf signal detection in nongaussian noise by a kurtosisbased. Model parameters are estimated using iterative procedures derived from the expectationmaximization em algorithm.
Acoustic impulsive noise based on nongaussian models. The primary signal is assumed to be random sequence of gaussian. The sensor noise was spatially correlated cyclostationary gaussian with same cycle frequency as the 3 nongaussian signals. Feasibility study on the least square method for fitting nongaussian.
Detectors for discretetime signals in nongaussian noise. Is there a mathematical method to determine if noise is. Pdf modeling of nongaussian colored noise and application in. Thus the probability density functions of the observations are assumed to be known, at least to within a finite number of unknown parameters in a known functional form. Nonlinear bayesian estimation of bold signal under nongaussian noise.
244 1392 188 102 525 7 219 202 900 534 1479 566 1553 1021 1053 1119 302 884 1304 483 301 568 141 41 1212 107 650 550 116 1553 46 163 677 771 1456 196 1416 564 817 498