a nonlinear optimum detection problem i theory an approximate log likelihood ratio for detecting a deterministic signal in linear and nonlinear gaussian noise is derived the new aspect of this detection problem is the inclusion of a nonlinear form of gaussian noise and a certain interaction between the signal and the linear noise such as modulation of the signal by the noise the derived log likelihood ratio consists of a modified version of the classical correlation detector and a new processor where the latter is an order of magnitude smaller than the former in the absence of the signal noise interaction the modification consists of subtracting the estimate of the nonlinear noise from the input to the correlation detector the nonlinear processor is a quadratic correlation processor where the correlation is not against the transformed signal but against the difference of two estimates of the nonlinear noise under the two hypotheses signal presence and signal absence presence of the signal noise interaction introduces further modification and complication on both processors

an optimum strategy for detection in the presence of random transient disturbance and white gaussian noise detection of a deterministic signal is considered because of the presence of the transient disturbance the traditional totally probabilistic criteria are no longer appropriate since they cannot be related to the physical reality as in the case of stationary ergodic noise only a mixed strategy is proposed for the detection where the disturbance is treated as a semideterministic object while white gaussian noise is treated as a genuinely random object specifically the author uses as the detection statistic the log likelihood ratio of two conditional probability measures corresponding to the signal presence and the signal absence hypotheses given the disturbance in which the disturbance is replaced by its maximum a posteriori likelihood estimate the detection statistic takes the generalized in function space form of a soft limiter or a blanker followed by the matched filter it is an explicit detection algorithm though improvement in computational efficiency is much desired

locally optimum detection in moving average non gaussian noise detection algorithms that are locally optimum bayes and also asymptotically optimum are developed for both coherent and incoherent signaling for arbitrary interference and signal waveforms when the dependence in the noise samples is represented by a moving average model this leads to receiver structures which are prewhitened versions of the locally optimum detectors in the independent case a probability of error expression in the ideal observer symmetric case the processing gain and the minimum detectable signal are derived in both cases these demonstrate by means of an expression comparing performance between this and the independent case that for the same large sample size n 1 an improvement in performance is always achieved when the noise samples are dependent without any additional complexity in receiver structure

on determining the radar threshold for non gaussian processes from experimental data the problem of detecting radar signals embedded in clutter is an area of great interest in many radar applications it is important to set thresholds to achieve a false alarm probability p sub f of 10 sup 5 or lower using conventional monte carlo techniques where thresholds are set based on raw percentiles an extremely large number of samples is required we use the generalized pareto distribution to approximate the extreme tail of the distributions and propose the ordered sample least squares osls method for estimating its parameters

spread spectrum multiple access performance of orthogonal codes impulsive noise a direct sequence spread spectrum multiple access ssma communication system that assigned a set of m orthogonal sequences to each user is analyzed an accurate model is incorporated for the impulsive noise that characterizes the lf and mf bands so that the ssma receiver operates in a combination of multiple access interference and impulsive atmospheric noise the performance of a linear receiver operating in such an environment is analyzed and probability of error curves are presented the presence of impulsive noise motivates the derivation and analysis of a nonlinear receiver that use a variable gain stage to suppress noise impulses this receiver is effectively optimum when the signal amplitudes are below a certain bound and when the noise and interference samples are independent or nearly so however the gain stage of this nearly optimum receiver depends on the noise model parameters including the various user delays consequently a nonparametric receiver that incorporates a simple clipper is also analyzed the asymptotic relative efficiency of both receivers is determined

locally optimum bayes detection in nonadditive non gaussian noise the locally optimum bayes theory of signal detection in additive non gaussian noise interference is extended to independent observations of the received data without the additive noise restriction the methodology employed parallels very closely the original development of threshold detection theory and utilizes the mathematical machinery of asymptotic decision theory especially the concepts of contiguity and locally asymptotically normal lan log likelihood ratio which are needed in the determination of the detector structure in both coherent and incoherent modes and its statistics under both hypotheses under the present framework the canonical in signal waveform and noise statistics optimum detection algorithms retain their asymptotically optimum character an example is provided in order to demonstrate the applicability of the theory to a specific noise environment where explicit forms of the non linearities involved and numerical values of the new noise indices are obtained moreover a significant improvement in performance 0 24 27 db over that of optimum detectors in independent additive gaussian and non gaussian noise is noted

optimum detection of fading signals in impulsive noise this paper deals with the synthesis and the analysis of optimum receivers to detect one out of m equally likely equi energy fading signals in impulsive noise modelled as a compound gaussian possibly correlated process we show that the conventional coherent and incoherent detectors are still optimum independent of the noise as well as the fading probability density functions the performance analysis has been carried on with reference to the general case of arbitrarily distributed disturbance in order to simplify the analysis asymptotical expressions have been developed for high signal to noise ratios as well as high signal space dimensionality interestingly enough this allows separating the effect of the noise spikyness from that of the fading law results indicate that for deep fading the noise marginal distribution does not dramatically affect the error probability nor is it influential on the limit operating characteristics corresponding to infinite signal space dimensions for non fluctuating signals instead the noise distribution plays a primary role spiky noise usually produces performance impairment moreover the limit performance in impulsive disturbance may exhibit marked deviations from the well known stepwise shape which is typical of gaussian channels

optimal and robust memoryless discrimination from dependent observations discrimination is considered between two possible sources based on dependent observations of their output the discrimination problem is modeled by means of a general binary hypothesis test the main emphasis being on situations that cannot be modeled as signals in additive noise the observations are modeled as stationary m dependent or rho mixing processes the structure of the discriminator is such that the observations are passed through a memoryless nonlinearity summed up to form a test statistic which is then compared to a threshold only fixed sample size tests are considered four different performance measures which resemble the signal to noise ratios encountered in the signal in additive noise problems are derived under different problem formulations the optimal nonlinearities for each of the performance measures are derived as solutions to various integral equations for three of the four performance measures the authors have successfully obtained robust nonlinearities for uncertainty in the marginal and the joint probability density functions of the observations computer simulation results that demonstrate the advantage of using these nonlinearities over the i i d nonlinearity under the probability of error criterion are presented

the modified cramer rao bound in vector parameter estimation in this paper we extend the scalar modified cramer rao bound mcrb to the estimation of a vector of nonrandom parameters in the presence of nuisance parameters the resulting bound is denoted with the acronym mcrvb where v stands for vector as with the scalar bound the mcrvb is generally looser than the conventional crvb but the two bounds are shown to coincide in some situations of practical interest the mcrvb is applied to the joint estimation of carrier frequency phase and symbol epoch of a linearly modulated waveform corrupted by correlated impulsive noise encompassing white gaussian noise as a particular case wherein data symbols and noise power are regarded as nuisance parameters in this situation calculation of the conventional crvb is infeasible while application of the mcrvb leads to simple useful expressions with moderate analytical effort when specialized to the case of white gaussian noise the mcrvb yields results already available in the literature in fragmentary form and simplified contexts

detection in multivariate non gaussian noise the applications of multivariate edgeworth series and higher order statistics to the discrete time detection of a known constant signal in multivariate non gaussian noise are considered a technique to derive suboptimum detectors from the neyman pearson optimum and locally optimum detectors is described a numerical algorithm based on knowledge of the noise cumulants is presented in order to analyze the finite sample size performance of the suboptimum detectors as an example the performance of the detectors as compared with the linear detector in multivariate gaussian gaussian mixture noise is presented via receiver operating characteristic curves numerical results indicate that the suboptimum detectors when exploiting knowledge of the dependence structure of the noise can have very good performance with respect to the linear detector

a heuristic self tuning fuzzy controller abstract in this paper a self tuning fuzzy controller based on the tuning of scaling factors is presented the tuning schemes are tuning the scaling factors is individual tuning rules the tuning rules are transformed into numeric tuning tables by applying suitable membership functions so that the adjustings of the scaling factors are merely matrix mappings since the tuning rules are constructed with the control signal and the output error and change in output error of the plant as their antecedent parts the tuning processes have the advantage of real time property simulation results with a perturbed servo and the inverted pendulum system as the controlled plants show the performance and adaptivity of the proposed controller

tuning fuzzy logic controllers using response envelope method a fuzzy logic controller flc is designed based on a human expert s knowledge of the process the performance of this initial design attempt will in general not be satisfactory in terms of certain design criteria such as steady state error the oscillatory behavior of the system etc this is due to the fact that no standard method exists for transforming human knowledge or experience into the rule base of the flc in this paper a method to tune the rule base of an initial flc design attempt is presented results of four experiments are reported and discussed

locally optimum bayes detection in nonadditive first order markov noise the purpose of this paper is to extend the recent formulation of locally optimum bayes lob detection in nonadditive non gaussian noise with independent sampling to the case where dependence between the noise samples is modeled via an ergodic first order markov discrete time process moreover unlike previous related work numerical results are provided which are based on an empirically derived second order transition density the marginal pdf of which is middleton s 1993 class a noise an experimentally verifiable and widely applicable non gaussian model canonical in signal waveform and noise statistics asymptotically and lob detectors in both coherent and incoherent modes are derived and their statistics are computed under both signal present and signal absent hypotheses performance measures are thus obtained together with the correlation gain g sup m p which is used for systems comparison explicit forms for the transition density and and the nonlinearities involved as well as numerical values of the noise indices are calculated from a generalized observation model containing multiplicative and additive markov noise components it is shown that significant performance gains over the case with independent sampling can be achieved depending upon the degree of correlation between the noise samples

a model reference control structure using a fuzzy neural network in this paper we present a design method for a model reference control structure using a fuzzy neural network we study a simple fuzzy logic based neural network system knowledge of rules is explicitly encoded in the weights of the proposed network and inferences are executed efficiently at high rate two fuzzy neural networks are utilized in the control structure one is a controller called the fuzzy neural network controller fnnc the other is an identifier called the fuzzy neural network identifier fnni adaptive learning rates for both the fnnc and fnni are guaranteed to converge by a lyapunov function the on line control ability robustness learning ability and interpolation ability of the proposed model reference control structure are confirmed by simulation results

stability in linear detection the stability of linear detectors for signals nominally known except for amplitude in correlated nominally gaussian noise is considered stability is defined in terms of bounds on the distribution of the test statistic as the true probability measure on the observation space varies in a prokhorov neighborhood of the nominal the derived stability index is independent of the neighborhood size an optimization problem is formulated and solved to give the most stable linear detector meeting a constraint on the nominal level of detectability

precision edge contrast and orientation estimation the contrast and orientation estimation accuracy of several edge operators that have been proposed in the literature is examined both for the noiseless case and in the presence of additive gaussian noise the test image is an ideal step edge that has been sampled with a square aperture grid the effects of subpixel translations and rotations of the edge on the performance of the operators are studied it is shown that the effect of subpixel translations of an edge can generate more error than moderate noise levels methods with improved results are presented for sobel angle estimates and the nevatia babu operator and theoretical noise performance evaluations are also provided an edge operator based on two dimensional spatial moments is presented all methods are compared according to worst case and rms error in an ideal noiseless situation and rms error under various noise levels

a maximum variance model for robust detection and estimation with dependent data a model that accounts for uncertain data dependency is developed by generating a large class of stationary stochastic processes each with the same univariate distribution this class can be considered to be a contamination class about the nominal independent and identically distributed i i d process distribution the class is developed specifically for application to robust detector and estimator design based on asymptotic variance application of this dependency class leads to an intuitively pleasing result the minimax variance estimators and the maximin efficacy detectors are the same as obtained using i i d asymptotic estimation and detection theory thus our technique generalizes previously obtained robust design results for i i d data to this dependent data case

an overview of sequence comparison time warps string edits and macromolecules a wide variety of different applications lead to problems in which sequences of different lengths must be compared to see how different they are and to see which elements in one sequence correspond to which elements in the other sequence successful methods for handling these problems been repeatedly reinvented which incorporate two basic ideas a systematic concept of distance between sequences and elegant recursive algorithms for doing the necessary computations the major application areas are speech processing and macromolecular biology computer science is another significant application area and applications have also been made to bird song handwriting analysis gas chromatography geological strata and text collation this paper surveys the applications methods and theory of sequence comparison

robust detection of signals a large deviations approach robust detection of a signal is considered for the case of independent and identically distributed observations following an asymptotic but nonlocal approach the exponential rates of decrease of the error probabilities are considered as measure of performance under this measure a robust detection structure for the symmetric density case is derived this detection structure is a generalization of an existing result for the local case and is reduced to it when the signal magnitude tends to zero

a recursive rule base adjustment algorithm for a fuzzy logic controller this paper introduces a recursive rule base adjustment to enhance the performance of fuzzy logic controllers here the fuzzy controller is constructed on the basis of a decision table dt relying on membership functions and fuzzy rules that incorporate heuristic knowledge and operator experience if the controller performance is not satisfactory it has previously been suggested that the rule base be altered by combined tuning of membership functions and controller scaling factors the alternative approach proposed here entails alteration of the fuzzy rule base the recursive rule base adjustment algorithm proposed in this paper has the benefit that it is computationally more efficient for the generation of a dt and advantage for online realization simulation results are presented to support this thesis

on convexity of error rates in digital communications convexity properties of error rates of a class of decoders including the maximum likelihood min distance one as a special case are studied for arbitrary constellations bit mapping and coding earlier results obtained for the additive white gaussian noise channel are extended to a wide class of noise densities including unimodal and spherically invariant noise under these broad conditions symbol and bit error rates are shown to be convex functions of the signal to noise ratio snr in the high snr regime with an explicitly determined threshold which depends only on the constellation dimensionality and minimum distance thus enabling an application of the powerful tools of convex optimization to such digital communication systems in a rigorous way it is the decreasing nature of the noise power density around the decision region boundaries that ensures the convexity of symbol error rates in the general case the known high low snr bounds of the convexity concavity regions are tightened and no further improvement is shown to be possible in general the high snr bound fits closely into the channel coding theorem all codes including capacity achieving ones whose decision regions include the hardened noise spheres from the noise sphere hardening argument in the channel coding theorem satisfy this high snr requirement and thus has convex error rates in both snr and noise power we conjecture that all capacity achieving codes have convex error rates convexity properties in signal amplitude and noise power are also investigated some applications of the results are discussed in particular it is shown that fading is convexity preserving and is never good in low dimensions under spherically invariant noise which may also include any linear diversity combining

detection in impulsive noise based on robust whitening despite the widespread use of the additive gaussian noise model it has long been recognized that many important environments are distinctly nongaussian the authors present a detector which is designed to operate in a correlated plus impulsive noise environment the detector robustly whitens the data and then uses a two sided threshold test to determine the presence of impulsive samples the impulsive samples are discarded and the remaining samples are used to detect the presence or absence of a signal using a matched filter an approximate analysis is presented and simulations are used to demonstrate the effectiveness of this approach

a fine tuning method for fuzzy logic rule bases abstract the problem of fine tuning the rule base of a fuzzy system is addressed in this paper a method is proposed that treats the rule base of the fuzzy system as a multivariate function and performs parameter optimization to approach the desired rule base output with the given set of rules the method is explained in detail and an application of it on two different rule bases is presented this work is part of a research effort in robotic precision assembly using fuzzy logic

locally optimum bayes detection in ergodic markov noise the locally optimum bayes theory of signal detection in non gaussian noise interference environments is extended to include ergodic markov noise models under mild regularity assumptions on the conditional probability density functions the proposed method expresses the log likelihood ratio under the null hypothesis via martingale limit theory as a locally asymptotically normal likelihood ratio which yields under the implied condition of contiguity the statistics of the detection algorithm under the alternative hypothesis thus optimum detection algorithms in both coherent and incoherent cases are obtained which are canonical in signal waveform and noise statistics and which have the desired property of asymptotic optimality acceptably small error probabilities as sample size becomes necessarily large while the terms in the taylor expansion of the log likelihood ratio about the null signal remain fixed furthermore locally optimum detection structures in gauss markov noise are given together with a specific example in the coherent mode of reception in order to demonstrate the significant improvement in performance obtained over independent sampling

adaptive threshold estimation via extreme value theory determining a detection threshold to automatically maintain a low false alarm rate is a challenging problem in a number of different applications the underlying parametric assumptions of most automatic target detection algorithms are invalid therefore thresholds derived using these incorrect distribution assumptions do not produce desirable results when applied to real sensor data monte carlo methods for threshold determination work well but tend to perform poorly when targets are present in order to mitigate these effects we propose an algorithm using extreme value theory through the use of the generalized pareto distribution gpd and a kolmogorov smirnov statistical test unlike previous work based on gpd estimates this algorithm incorporates a way to adaptively maintain low false alarm rates in the presence of targets both synthetic and real world detection results demonstrate the usefulness of this algorithm

fuzzy control theory a nonlinear case abstract sources of nonlinearity in a fuzzy controller include the fuzzification algorithm used for the controller inputs fuzzy control rules type of fuzzy logic used for evaluating the fuzzy control rules and the defuzzification algorithm used for the controller output we analyze the performance of a simple fuzzy controller with linear and nonlinear defuzzification algorithms we prove theoretically that such a fuzzy controller the smallest possible with two inputs error and rate change of error and a nonlinear defuzzification algorithm is equivalent to a nonfuzzy nonlinear proportional integral pi controller with proportional gain and integral gain changing with error and rate change of error about a setpoint furthermore this fuzzy controller is precisely equivalent to a conventional linear pi controller if a linear defuzzification algorithm is employed computer simulation showed that the performance of the fuzzy controller was almost the same as that of the pi controller when first order and second order linear processes were used furthermore the fuzzy controller was significantly better when a first order with a time delay model was used more importantly the simulated result illustrated that the fuzzy controller was stable when a nonlinear process model was controlled but the pi controller was unstable

detection of dependent heavy tailed signals this paper examines the problem of detection of dependent alpha stable signals measurements of several phenomena exhibit non gaussian heavy tailed behavior in their probability density functions p d f we use the class of alpha stable distributions to characterize these signals when two sensors make simultaneous measurements of such phenomena these heavy tailed realizations are dependent across sensors the intersensor dependence is modeled using copulas we consider a two sided test in the neyman pearson framework and present an asymptotic analysis of the generalized likelihood test glrt both nested and non nested models are considered in the analysis the performance of the proposed scheme is evaluated numerically on simulated data as well as indoor seismic data with appropriately selected models our results demonstrate that a high probability of detection can be achieved for false alarm probabilities of the order of 10 4

noise benefits to robust m estimation of location in dependent observations abstract in the presence of weakly dependent noise we analyze the asymptotic efficiency of robust maximum likelihood type estimators m estimators for location estimation we observe the noise enhanced asymptotic efficiency effect by tuning the background noise level for a single m estimator we also theoretically derive the asymptotic efficiency of a parallel array of m estimators and illustrate the possibility of the noise enhancement of the asymptotic efficiency by injecting extra additive noise components these obtained results enrich the research of noise benefits for the robust estimation of location from the dependent observations

robust locally optimum detection of signals in dependent noise a robust locally optimum detector of a signal embedded in additive dependent nongaussian noise is presented the performance criterion is bayes risk the sample size is finite and the uncertainty class of multivariate inputs is the in contamination model the locally optimum detector is shown to be a censored version of the nominal likelihood ratio

insight into detection of deterministic and gaussian signals in correlated plus impulsive noise environments the authors present a different view of the discrete time detection problem in impulsive noise the approach used is to examine the geometry from a subspace viewpoint of two detectors the clairvoyant matched filter and the clairvoyant power detector they are clairvoyant in that the locations of the impulses in the received data sample are assumed known in the presence of impulses both detectors use this knowledge for properly weighting these samples to reduce the effects of the impulses unfortunately since it is never reasonable to assume that the impulse locations are known these are unrealizable detectors however these clairvoyant detectors provide insight into the problem of detection in an impulsive environment when the nominal component is correlated and suggest detectors which are realizable the results can be used as a theoretical justification of preprocessing the data to eliminate impulsive samples prior to detection

detection in correlated impulsive noise using fourth order cumulants we consider detection and estimation in correlated impulsive noise the non gaussian impulsive noise is modeled as the sum of two linear processes a nominal part and an impulsive part this model admits correlated impulsive bursts lasting many data samples identifiability of the noise model is established using fourth and second order cumulants under this model the correlated time series can be whitened and an appropriate memoryless nonlinearity applied to attenuate the impulsive events a detection statistic is then formed from the output of the nonlinearity in the threshold detection case the use of cumulants allows identification of the noise in the presence of the signal to be detected obviating the need for noise only training records simulation results with a sample size of 512 show small loss in detector performance versus an ideal detector with no impulsive part present

detection of narrow band signals in spherically invariant noise this paper deals with noncoherent discrete time detection of a narrow band 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 at first an asymptotic sufficient statistic for an arbitrary fading law is derived then the asymptotically optimum detector for rayleigh distributed amplitude fluctuations is synthesized the detection structure implementation requires the knowledge but for a scale factor of the correlation function of the noise but is independent of the distribution function of the modulating random variable the performance of the asymptotically optimum detector synthesized for rayleigh fading is assessed via computer simulations the results show that the performance degradation with respect to the fully optimum performance is scarcely significant even for low values of the sample size moreover in highly non gaussian noise the proposed detector largely outperforms the fully optimum detector synthesized under the correlated gaussian noise assumption

min max detection of weak signals in phi mixing noise detection of weak signals in a special varphi mixing noise class is considered the detector structure is restricted to sums of memoryless nonlinear transformations of the observations correlated with the data sequence and compared to a fixed threshold using the efficacy to measure performance the nonlinearity that has min max performance is derived

improving classifier performance through repeated sampling abstract in traditional pattern recognition the classification decision is based on a single observation of the input in this paper we show that by relaxing this assumption the performance of the classifier can be improved substantially we present a detailed analysis of one particular method for achieving this taking a consensus vote on the classifier s output for repeated samples of the input we prove that this approach always yields a net improvement in recognition accuracy for common distributions of interest upper and lower bounds on the improvement are also discussed under certain conditions it is even possible to beat the bayes error bound associated with the classifier we conclude by presenting results from three sets of experiments examining the effectiveness of the idea

a self tuning fuzzy controller abstract the aim of a fuzzy controller is to compensate the dynamic characteristics of the controlled system it is the same purpose as for the fuzzy logic controller flc now the best design of the flc is required for achievement of this purpose but such a controller has not yet been completely designed because the fuzzy controller is constructed of control rules which are ambiguously described by conventional controller strategies and expert knowledge and because its structure theory is not well established yet that is in order to construct the best rules a highly skilled technique and a tuning technique using trial and error are required as a way to easily design a flc a method which improves a given standard flc is considered in this paper we propose a self tuning algorithm of the flc it has two functions in adjusting the scaling factors which are the parameters of the flc and in improving the control rules of flc by evaluating the control response at real time and the control results after operations we add these functions to the flc and design the self tuning fuzzy controller stfc this makes the flc a good controller

a computational approach to edge detection this paper describes a computational approach to edge detection the success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points these goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution we define detection and localization criteria for a class of edges and present mathematical forms for these criteria as functionals on the operator impulse response a third criterion is then added to ensure that the detector has only one response to a single edge we use the criteria in numerical optimization to derive detectors for several common image features including step edges on specializing the analysis to step edges we find that there is a natural uncertainty principle between detection and localization performance which are the two main goals with this principle we derive a single operator shape which is optimal at any scale the optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a gaussian smoothed image we extend this simple detector using operators of several widths to cope with different signal to noise ratios in the image we present a general method called feature synthesis for the fine to coarse integration of information from operators at different scales finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge

optimum diversity detection over fading dispersive channels with non gaussian noise we consider the problem of m ary signal detection over a single input multiple output simo channel affected by frequency dispersive rayleigh distributed fading and corrupted by additive non gaussian noise modeled as a spherically invariant random process we derive both the optimum detection structure and a suboptimal reduced complexity receiver based on the low energy coherence approach interestingly both detection structures are canonical i e they are independent of the actual noise statistics we also carry out a performance analysis of both receivers with reference to the case that the channel is affected by a frequency selective fading and for a binary frequency shift keying signaling format the results obtained through both a chernoff bounding technique and monte carlo simulations reveal that the adoption of diversity also represents a suitable means to restore performance in the presence of dispersive fading and impulsive non gaussian noise interestingly it is also shown that the suboptimal receiver incurs a limited loss with respect to the optimum unrealizable receiving structure

locally optimal detection in multivariate non gaussian noise the detection of a vanishingly small known signal in multi variate noise is considered efficacy is used as a criterion of detector performance and the locally optimal detector lod for multivariate noise is derived it is shown that this is a generalization of the well known lod for independent identically distributed i i d noise several characterizations of multivariate noise are used as examples these include specific examples and some general methods of density generation in particular the class of multivariate densities generated by a zero memory nonlinear transformation of a correlated gaussian source is discussed in some detail the detector structure is derived and practical aspects of obtaining detector subsystems are considered through the use of monte carlo simulations the performance of this system if compared to that of the matched filter and of the i i d lod finally the class of multivariate densities generated by a linear transformation of an i i d noise source is described and its lod is shown to be a form frequently suggested to deal with multivariate non gaussian noise a linear filter followed by a memoryless nonlinearity and a correlator

on signal detection in the presence of weakly correlated noise over fading channels in cognitive radio networks in order to avoid interference form secondary users to the primary license holders of the spectrum reliable spectrum sensing is necessary in scenarios where the noise samples are correlated the spectrum sensing methods optimized considering impairment by independent noise samples will not provide optimum performances to address this issue a locally optimum detection method for random signals under a weakly correlated noise model over fading channels is proposed the probabilities of false alarm and detection of the proposed detector in the low signal to noise ratio regime are analyzed the average probabilities are calculated over different channel gains numerical and simulation results demonstrate the superiority of the proposed method over the known energy detection method with comparable complexities furthermore we consider the scenario where the estimated correlation is different from the real correlation and investigate the effect of this correlation mismatch on the probabilities of false alarm and detection of the proposed method

an adaptive spatial diversity receiver for non gaussian interference and noise standard linear diversity combining techniques are not effective in combating fading in the presence of non gaussian noise an adaptive spatial diversity receiver is developed for wireless communication channels with slow flat fading and additive non gaussian noise the noise is modeled as a mixture of gaussian distributions and the expectation maximization em algorithm is used to derive estimates for the model parameters the transmitted signals are detected using a likelihood ratio test based on the parameter estimates the new adaptive receiver converges rapidly its bit error rate performance is very close to optimum when relatively short training sequences are used and it appears to be relatively insensitive to mismatch between the noise model and the actual noise distribution simulation results are included that illustrate various aspects of the adaptive receiver performance

a pd like self tuning fuzzy controller without steady state error abstract this paper presents a pd like self tuning fuzzy controller stfc based on the tuning of scaling factors a two stage tuning method including a direct tuning stage and indirect tuning stage is proposed for most linear controlled plants application of the direct tuning stage alone is enough to yield satisfactory system responses the proposed stfc can automatically detect the operating ranges of input variables and then adjust the scaling factors in the indirect tuning stage the back propagation algorithm can be directly applied to fine tune the scaling factors when a more complicated plant is controlled furthermore a simple but efficient method for fully compensating for steady state error which is the main problem in a pd like fuzzy logic control system is proposed so that tuning for fuzzy logic rule bases is unnecessary simulation results with linear time invariant systems and a nonlinear unstable system as the controlled plants show that the proposed technique yields zero steady state error responses very quickly without overshoot or oscillatory behavior

non gaussian noise models in signal processing for telecommunications new methods an results for class a and class b noise models the subject here is generalized i e non gaussian noise models and specifically their first order probability density functions pdfs attention is focused primarily on the author s canonical statistical physical class a and class b models in particular class a noise describes the type of electromagnetic interference emi often encountered in telecommunication applications where this ambient noise is largely due to other intelligent telecommunication operations on the other hand ambient class b noise usually represents man made or natural nonintelligent i e nonmessage bearing noise and is highly impulsive class a noise is not an spl alpha stable process nor is it reducible to such except in the limiting gaussian cases of high density noise by the central limit theorem class b noise is also asymptotically normal before model approximation under rather broad conditions principally governed by the source propagation and distribution scenarios the pdf of class b noise alone no gaussian component can usually be approximated by 1 a symmetric gaussian spl alpha stable s spl alpha s model in the case of narrowband reception or when the pdf spl omega sub 1 spl alpha of the amplitude is symmetric and 2 a nonsymmetric spl alpha stable ns spl alpha s model no gaussian component can be constructed in broadband regimes new results here include i counting functional methods for constructing the general qth order characteristic functions cfs of class a and class b noise from which all moments and in principle the pdfs follow ii the first order cfs pdfs and cumulative probabilities apds of nonsymmetric broadband class b noise extended to include additive gauss noise agn iii proof of the existence of all moments in the basic class a and class b models iv the key physical role of agn and the fact that agn removes spl alpha stability v the explicit roles of the propagation and distribution scenarios and vi extension to noise fields although telecommunication applications are emphasized class a and class b noise models apply selectively but equally well to other physical regimes e g underwater acoustics and em radar optics etc supportive empirical data are included

detection of random signals in gaussian mixture noise a locally optimal detection algorithm for random signals in dependent noise is derived and applied to independent identically distributed complex valued gaussian mixture noise the resulting detector is essentially a weighted sum of power detectors the power detector is the locally optimal detector for random signals in gaussian noise the weighting functions are modified to enhance the detection performance for small sample sizes an implementation of the mixture detector using the expectation maximization algorithm is described moments of these detectors are calculated from piecewise polynomial approximations of the weighting functions the sum of sufficiently many independent identically distributed detector outputs is then approximated by a normal distribution probability distributions are also derived for the power detector in gaussian mixture noise for a particular set of noise parameters the theoretical distributions are compared with those obtained from monte carlo simulation and seen to be quite close the theoretical distributions are then used to compare the performance of the mixture and power detectors in gaussian mixture noise over a range of parameters and to assess the impact of parameter error on detection performance in this study the signal gain of the mixture detectors varies from 15 to 38 db and the degradation of the probability of detection due to parameter estimation error is relatively minor

robust detection of signals in dependent noise the robust detection of signals in additive dependent noise is considered the solution to the finite sample problem is obtained when the bayes risk is used as the performance measure for the multivariate densities involved we assume that they belong to an e contamination model the robust detection structure is shown to be optimum for the least favorable density and is a censored version of the nominal likelihood ratio

application of the volterra functional expansion in the detection of nonlinear functions of gaussian processes detection of a memoryless nonlinear functional of a gaussian process in additive gaussian white noise is considered the volterra functional expansion for the likelihood ratio and two examples of calculating the kernels are presented it is shown that kernels up to third order can be obtained for a hard limited gaussian process and for the absolute value of a gaussian process for the case of hard limiting the kernels are nonlinear functions of the autocorrelation of the gaussian process for the absolute value case the kernels are nonlinear functions of the kernel derived for the linear problem a monte carlo simulation of receiver performance is presented for the case of detection of the absolute value of a first order butterworth process in additive gaussian white noise the suboptimal detector is obtained by truncating the log likelihood ratio to second order

on the analysis of fuzzy logic controllers abstract in this paper we address the issue of analyzing the properties of the fuzzy logic controller flc we introduce two basic models of the flc the first representation uses the concept of the quasilinear fuzzy model the flc is approximated by a virtual pid controller consisting of linear controller connected in parallel the second interpretation of the flc is from the point of view of nonlinear control the flc is analyzed as a sliding mode like controller based on these two alternative representations are derived rules for designing and tuning fuzzy logic controllers

postfiltering versus prefiltering for signal recovery from noisy samples we consider the extension of the whittaker shannon ws reconstruction formula to the case of signals sampled in the presence of noise and which are not necessarily band limited observing that in this situation the classical sampling expansion yields inconsistent reconstruction we introduce a class of signal recovery methods with a smooth correction of the interpolation series two alternative data smoothing methods are examined based either on a global postfiltering or a local data presmoothing we assess the accuracy of the methods by the global l sub 2 error both band limited and non band limited signals are considered a general class of correlated noise processes is taken into account the weak and strong rates of convergence of the algorithms are established and their relative efficiency is discussed the influence of noise memory and its moment structure on the accuracy is thoroughly examined

on the robust estimation of the autocorrelation coefficients of stationary sequences this paper discusses methods for the estimation of the autocorrelation coefficients of a finite dependent stationary random sequence three estimators are examined the sample average and two proposed approaches namely the pseudo maximum likelihood pseudo ml estimator and the pseudo m estimator the latter scheme is found as a solution of a fredholm integral equation all three estimators are first studied for specific distribution models then the existence of a minimax robust design is proved and a suboptimally robust scheme is proposed simulation results illustrate the theoretical foundations of the methods and indicate that the pseudo m estimator achieves significantly better performance than the other two schemes when tested against dependent data and in the presence of outliers finally the results may also be applied to the estimation of a location parameter of a dependent random sequence

signal detection in the presence of weakly dependent noise i optimum detection the problem of designing robust systems for detecting constant signals in the presence of weakly dependent noise with uncertain statistics is considered as in part i of this study which treated optimum detection in weakly dependent noise a moving average representation is used to model the dependence structure of the noise process with the degree of dependence being parameterized by the averaging weights weak dependence is then modeled as the situation in which quantities depending to second or higher order on the averaging weights can be considered to be negligible uncertainty in the noise statistics is introduced within this framework by allowing a general type of uncertainty in the univariate statistics of the independent sequence that drives the moving average to find robust detectors for signals in this type of weakly dependent noise environment related results concerning robust location estimation in an analogous dependent situation are applied to modify a robust detection system for the corresponding independent noise case it is argued here that a robust detector for this dependent noise model is characterized by a least favorable noise distribution which coincides with the distribution that is least favorable for the corresponding independent noise case however the resulting detector design for dependent noise differs from that for independent noise in particular the robust detector for dependent noise is based on a linearly corrected version of the influence curve that defines the independent noise robust detector the worst case performance of the proposed robust detector relative to that of the independent noise robust detector is also analyzed with the conclusion that the performance of the proposed technique is better to first order in the averaging weights in this respect a modification of this robust detector is also proposed which eliminates some practical disadvantages of this system while retaining equivalent performance to first order the specific situation of contaminated gaussian noise is treated in order to illustrate the analysis

optimum detection over rayleigh fading dispersive channels with non gaussian noise the paper discusses the problem of detecting one out of m gaussian correlated signals in impulsive noise modeled as a compound gaussian possibly correlated process we first show that for uncorrelated noise the detection problem admits one and the same optimum in the sense of attaining minimum error probability solution independent of the noise statistics the optimum detector in fact amounts to an estimation block aimed at measuring the short time noise power spectral density psd whose output is fed to a bank of estimators correlators each keyed to one of the m admissible waveforms and to the estimated noise psd we also give suggestions for realizing a suboptimum receiver with reduced complexity which again is canonical in its structure as for the performance analysis we focus on binary frequency shift keying bfsk signaling we provide numerical results for two particular channel correlations and for cauchy distributed noise these results indicate that like for the general gauss gauss case the performance depends on the energy contrast as well as on the time bandwidth product of the useful signal moreover noise spikiness seems to negatively affect the performance in the sense that heavier and heavier high amplitude tails of the noise marginal distribution give rise to higher and higher error probabilities for fixed energy contrast and time bandwidth signal product

asymptotic performance of m ary orthogonal modulation in generalized fading channels the asymptotic m to infinity probability of symbol error p sub e sub m for m ary orthogonal modulation in a nakagami m fading channel is given by the incomplete gamma function p m mx where x in 2 e sub b n sub 0 and e sub b is the average energy per bit for large signal to noise ratio this leads to a channel where the probability of symbol error varies as the inverse mth power of e sub b n sub 0 these channels exist for all m or 1 2 the special case of m 1 corresponds to rayleigh fading an inverse linear channel

a fuzzy decision problem based on the generalized neyman pearson criterion korea science and engineering foundation kosef under grant 893 0801 011 2 and korea advanced institute of science and technology kaist under an internal grant

optimum detection of a weak signal with minimal knowledge of dependency the optimum nonlinearity is defined for detection of a weak signal when minimal knowledge of the dependency structure of the observations is available specifically it is assumed that the observations form a one dependent strictly stationary sequence of random variables and that only a finite number of moments of the marginal density and the correlation coefficient between consecutive observations are known it is assumed that the bivariate densities involved can be represented as diagonal series using orthonormal polynomials using efficacy as a performance measure the optimum nonlinearity is required to satisfy a saddle point condition over this class of bivariate densities

estimating the stable index alpha in order to measure tail thickness a critique stable laws are often fit to outlier prone data and if the index alpha is estimated to be much less than two then the normal law is rejected in favor of an infinite variance stable law this paper derives the theoretical properties of such a procedure when the true distribution is stable the distribution of the m l e of alpha is non regular if alpha 2 when the true distribution is not stable the estimate of alpha is not a robust measure of the rate of decrease of the tail probabilities a more robust procedure is developed and a statistic for describing and comparing the tail shapes of arbitrary samples is proposed