A general introduction to signal detection in nongaussian noise can be found in ref. 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. If a clean speech signal is corrupted by additive gaussian noise n, its probability density function can be expressed as. Locally optimum detection of a noise model based on. Orthogonal polynomial approximation, signal detection and estimation, non gaussian noise 1 introduction transformation method. Joint signal parameter estimation in nongaussian noise by. In this second part of an ongoing study, the general problem of optimum and suboptimum detection of threshold i. Sequential strategy to solve the problem of sequential detection for unknown 1, wald proposed two possible solutions. The new result in this paper is the use of the non gaussian noise.
Stochastic resonance with colored noise for neural signal. The problem of detecting the presence of a random signal embedded in additive correlated nongaussian noise modeled as a spherically invariant random process is. This paper deals with noncoherent discretetime detection of a narrowband signal subject to slow and nonselective fading and embedded in correlated nongaussian noise modeled as a spherically invariant random process whose modulating random variable is continuous. For the case of independent non gaussian noise samples, the theory of locally opdimum bayes detection lobd. 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. Red i am attaching screens to be processed to noise.
Therefore, we accomplish the nongaussian signal detection by using. A general introduction to signal detection in non gaussian noise can be found in ref. Generally, nongaussian detection problems are analytically intractable and. T1 detection and estimation of chirp signals in nongaussian noise. Ndimensional probability density function is binary orthogonal modu lation on.
Estimation of the parameters of sinusoidal signals in nongaussian noise, ieee trans actions on signal processing 57 no. Procedia apa bibtex chicago endnote harvard json mla ris xml iso 690 pdf. Of course the focus is on noise which is not gaussian. Pdf some univariate noise probability density function models. In such nongaussian interference, the detection key is to. 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. Detection of random signals in gaussian mixture noise article pdf available in ieee transactions on information theory 416. Random signal detection in correlated nongaussian noise.
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 most practical situations, the signal is nongaussian or becomes nongaussian after going through a nonlinear propagation media. The detector has been tested and applied on an underwater. Pdf detection of random signals in gaussian mixture noise. If the signal is nongaussian, np detector does not give promising results.
Kassam conditional tests in nonparametric detection in nonparametric methods in. Gps signal detection under multiplicative and additive noise. We will further assume that both xn and n have zero means and that they are statistically independent. Schwartz department of electrical engineering and computer science princeton university princeton, nj 08544 abstract in this report, we study procedures for robust detection of slowly fading narrow. Detection and estimation of chirp signals in nongaussian. This is the detection of signals in addi tive noise which is not required to have gaussian. The majority of the signal detection and modulation classification algorithms available in the literature assume that the additive noise has a gaussian distribution. W and divergence with binary communication system where cannot. Preprocessing of data by the in the signal detection and estimation problems, we often assume that the additive random noise process is gaussian. However, these detectors require the knowledge of the noise probability density function pdf. Regazzoni2 department of biophysical and electronic engineering dibe, university of genoa via allopera pia 11a 16145 genova italy phone. In the signal detection and estimation problems, we often assume that the.
At first, an asymptotic sufficient statistic for an arbitrary fading law is derived. Kassam, signal detection in nongaussian noise, springer. Pdf signal detection in nongaussian noise by a kurtosisbased. Kassam, optimum quantization for signal detection, ieee trans actions on. Noiseenhanced nonlinear detector to improve signal detection.
Single cycle and locallyoptimum multicycle detectors are proposed. Actually, in many real applications in the fields of seismology, underwater acoustics, electromagnetic telecommunications, etc. Robust detection of fading narrowband signals in nongaussian noise m. Toward the detection of gravitational waves under non. Dec 11, 2014 signal detection under weakly nongaussian noise distribution since gaussian noise is fully characterized by the covariance matrix or the twobody correlation function, any nonvanishing higherorder cumulants or reduced correlation functions are signatures of nongaussianity of the probability distribution function pdf, p x. Comparison of bistable systems and matched filters in non. A robust detector of known signal in nongaussian noise using. This book contains a unified treatment of a class of problems of signal detection theory. Hosbased noise models for signaldetection optimization.
The models are used in the design of a lod test for detecting weak signals in real nongaussian noise. It is noted that, at the uniform noise level, the detection efficacy in eq. It can be applied either under the ideal but often not realistic assumption of gaussian background noise, or on the basis of realistic statistical models of channel noise. The pdf model is expressed in terms of a fourthorder statistical parameter. Possible applications to noise pdf modeling are the optimization of signal detection, parameter estimation, classification, etc. Hosbased generalized noise pdf models for signal detection.
Polynomial transformation method for nongaussian noise. Kassam, signal detection in nongaussian noise, springerverlag. Detection of random signals in additive noise springerlink. The primary accomplishmentswere in the areas of i signal detection in nongaussian noise, including nonparametric and robust detection. Cyclostationary detection in nongaussian noise has been considered in 14. For this purpose, a novel gps signal detection scheme based on high order cyclostationarity is proposed.
The essential feature of stochastic resonance is the performance enhancement of nonlinear systems by an appropriate nonzero noise level 519. In this paper, we consider the mai mitigation problem in dscdma channels with nongaussian ambient noise. Sequential strategy to solve the problem of sequential detection for unknown 1. It may enter the receiver through the antenna along with the desired signal or it may be generated within the receiver. 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. In this paper, we generate colored gaussian noise, colored nongaussian 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. Signal detection under weakly nongaussian noise distribution. Nongaussian noise benefits for coherent detection of. Signal detection and estimation artech house radar library hardcover. 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. Gaussian pdf, the middleton class a pdf, and some such. 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. N2 detection of chirps in non gaussian additive and multiplicative noise is explored via a novel cyclostationary approach.
Some univariate noise probability density function models. Under what circumstances is a nonuniform quantizer. Generalized detector, constant false alarm rate, detection performance, gaussian noise, radar. An introduction to signal detection and estimation springer. The locally optimum lo criterion is selected from a large number of detection criteria. Detection of narrowband signals in spherically invariant noise. We investigate the nongaussian signal detection in gaussian 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 nongaussian environments. There have been different statistical distributions proposed to model such impulsive noise such as the. Detection of narrowband signals with random phase angles. Blum, \on the optimality of nite level quantizations for distributed signal detection, ieee transactions on information theory, pp. However, the computational complexity of ml detection is quite high, and therefore, effective nearoptimal multiuser detection techniques in nongaussian noise are needed. Robust signaltonoise ratio estimation based on waveform.
Radar signal detection in nongaussian noise using rbf neural. Kassam, signal detection in nongaussian noise, springerverlag, new york, 1988. 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. Detection of narrowband signals in spherically invariant. 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. Signal detection and modulation classi cation in nongaussian. Here our signal will be modeled entirely as a non deterministic random process. Quadratic tests for detection of abrupt changes in. Hosbased noise models for signaldetection optimization in nongaussian environments a. Signal detection in nongaussian noise, sprin ger verlag, 1988. Simulations for density click here conditions and probability. The principle is introduced, the gps signal detection structure is described, the ambiguity of initial pseudorandom noise prn code phase and doppler shift of gps signal is analysed.
One of the primary uses of higher order statistics in signal processing has been for detecting and estimation of non gaussian signals in gaussian noise of unknown covariance. Frequency estimation of fm signals under nongaussian and. Pro auto system where pdf, fundamental analysis applied. Moreover, the multicycle detector requires the knowledge of the signal phase as well. Introduction the detection of signals in the presence of noise is a significant problem that arises in various signal processing applications, such as. The principle is introduced, the gps signal detection structure is described, the ambiguity of initial pseudorandom noise prn code. Introduction the detection of signals in the presence of noise is a significant problem that arises in various signal processing applications, such as radar and sonar systems. On optimal threshold and structure in threshold system based detector. Different models can also be used to model different noisetypes such as the gaussian, poisson, impulsive, nongaussian models among others 3. This comparison is meaningful since the linear detectors are often used even when the noise is a priori known to be nongaussian. We investigate the non gaussian signal detection in gaussian noise.
Robust multiuser detection in nongaussian channels. Signal detection and modulation classi cation in non. The new result in this paper is the use of the nongaussian noise. Regazzoni dibe, university of genoa, genoa, italy abstract two pdf models suitable for describing nongaussian iid noise are introduced.
In most practical situations, the signal is non gaussian or becomes non gaussian after going through a nonlinear propagation media. Nearly optimal detection of signals in nongaussian noise dtic. Kassam has a number of ieee papers on the topic, and since you are a student, both the book and papers are probably available. Signal detection and modulation classification in non. Following the scheme of our development so far, the focus will be on the detection of a random signal in additive white noise. Detection in nongaussian noise university of washington. By using this noise model, the robustness of various detection strategies can be assessed. This paper deals with noncoherent discretetime detection of a narrowband signal subject to slow and nonselective fading and embedded in correlated non gaussian noise modeled as a spherically invariant random process whose modulating random variable is continuous. Signal detection by generalized detector in compoundgaussian. In order to optimize signal detection in nongaussian environments, the work is addressed to provide realistic modeling of a generic noise probability density.
Vincent poor essential background reading for engineers and scientists working in such fields as communications, control, signal, and image processing, radar and sonar, radio astronomy, seismology, remote sensing, and instrumentation. 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. Since gaussian noise is fully characterized by the covariance matrix or the twobody correlation function, any nonvanishing higherorder cumulants or reduced correlation functions are signatures of nongaussianity of the probability distribution function pdf, px. A robust detector of known signal in nongaussian noise. Betz, detection of weak random signals in iid nongaussian noise, ieee trans. For a given gaussian noise level, it is shown in figs. Pdf radar signal detection in nongaussian noise using rbf. The model depends on few parameters which can be estimated quickly and easily, and so general to be able to describe many kinds of noise such as symmetric or asymmetric.
Detection of binary signal in gaussian noise pdf investing post. Therefore, we accomplish the non gaussian signal detection by using. Signal detection in nongaussian noise springerlink. Additionally, however, a brief discussion of narrowband random signal detection in narrowband noise is included in this chapter. Hosbased noise models for signaldetection optimization in. The problem of hosbased signal detection methods applied in real communication systems is addressed. This is the detection of signals in additive noise which is not required to have gaussian probability density functions in its statistical description. The obtained detection structure does not depend on the noise univariate probability density function pdf. Kassam, signal detection in nongaussian noise, springer verlag. Pdf optimum reception in nongaussian electromagnetic. Pdf signal detection in nongaussian noise by a kurtosis. An introduction to signal detection and estimation springer texts in electrical engineering h. However,it requires the knowledge,but for a scale factor, of the noise correlation matrix. Both signal processing algorithms and performance measures are obtained canonically, and specifically when the electromagnetic interference environment emi is.
1161 1503 673 119 566 1540 1511 1372 97 674 529 1455 1535 1441 951 1014 209 1064 15 1259 181 1098 1607 1006 1368 468 1130 1533 787 849 404 1556 827 526 1581 384 239 909 859 471 1474 38 884 60 980