Showing papers on "White noise published in 1997"
TL;DR: It is proved in this two-user case that the probability of error of the MMSE detector is better than that of the decorrelating linear detector for all values of normalized crosscorrelations not greater than 1/2 /spl radic/(2+/spl Radic/3)/spl cong/0.9659.
Abstract: The performance analysis of the minimum-mean-square-error (MMSE) linear multiuser detector is considered in an environment of nonorthogonal signaling and additive white Gaussian noise. In particular, the behavior of the multiple-access interference (MAI) at the output of the MMSE detector is examined under various asymptotic conditions, including: large signal-to-noise ratio; large near-far ratios; and large numbers of users. These results suggest that the MAI-plus-noise contending with the demodulation of a desired user is approximately Gaussian in many cases of interest. For the particular case of two users, it is shown that the maximum divergence between the output MAI-plus-noise and a Gaussian distribution having the same mean and variance is quite small in most cases of interest. It is further proved in this two-user case that the probability of error of the MMSE detector is better than that of the decorrelating linear detector for all values of normalized crosscorrelations not greater than 1/2 /spl radic/(2+/spl radic/3)/spl cong/0.9659.
890 citations
TL;DR: In this article, a robust nonlinear control toolbox includes a number of methods for systems affine in deterministic bounded disturbances, but the problem when the disturbance is unbounded stochastic noise has hardly been considered.
572 citations
TL;DR: In this paper, the authors analyzed time series of daily positions estimated from data collected by 10 continuously monitoring Global Positioning System (GPS) sites in southern California during the 19-month period between the June 28, 1992 (Mw=7.3), Landers and January 17, 1994 (mw=6.7), Northridge earthquakes.
Abstract: We analyze time series of daily positions estimated from data collected by 10 continuously monitoring Global Positioning System (GPS) sites in southern California during the 19-month period between the June 28, 1992 (Mw=7.3), Landers and January 17, 1994 (Mw=6.7), Northridge earthquakes. Each time series exhibits a linear tectonic signal and significant colored noise. Spectral power at frequencies in the range 5 yr−1 to 0.5 d−1 is dominated by white noise or possibly fractal white noise and is several orders of magnitude higher than what would be expected from random walk noise (in this short-period range) attributed by others to geodetic monument motions. Estimating a single slope for the time series' power spectra suggests fractal white noise processes with spectral indices of about 0.4. Site velocity uncertainties assuming this fractal white noise model are 2–4 times larger than uncertainties obtained assuming a purely white noise model. A combination white noise plus flicker noise (spectral index of 1) model also fits the data and suggests that the velocity uncertainties should be 3–6 times larger than for the white noise model. We cannot adequately distinguish between these two noise models, nor can we rule out the possibility of a random walk signal at the lowest frequencies; these questions await the analysis of longer time series. In any case, reducing the magnitude of low-frequency colored noise is critical and appears to be best accomplished by building sites with deeply anchored and braced monuments. Otherwise, rate uncertainties estimated from continuous GPS measurements may not be improved significantly compared to those estimated from infrequent campaign-mode measurements.
461 citations
TL;DR: In this article, a curve-fitting cross-correlation function between two response measurements made on an ambiently excited structure is shown to have the same form as the system's impulse response function.
364 citations
TL;DR: In this paper, the authors analyzed field collected ambient noise by solving the Wiener-Hopf linear prediction equations to estimate the modal frequency and damping, and compared the results with results from a Prony analysis on a ringdown resulting from a 1400 MW brake insertion under the same operating conditions as the ambient data.
Abstract: Power system loads are constantly changing. Over a time-span of a few minutes, these changes are primarily random. The random load variations act as a constant low-level excitation to the electromechanical dynamics of the power system which shows up as ambient noise in field measured voltage, current and power signals. Assuming the random variations are white and stationary over an analysis window, it is theoretically possible to estimate the electromechanical modal frequencies and damping from the spectral content of the ambient noise. In this paper, field collected ambient noise is analyzed by solving the Wiener-Hopf linear prediction equations to estimate the modal frequency and damping. These estimates are then compared with results from a Prony analysis on a ringdown resulting from a 1400 MW brake insertion under the same operating conditions as the ambient data. Results show that estimates are consistent between the ambient and ringdown analysis indicating that it is possible to estimate a power system's electromechanical characteristics simply from ambient data. These results demonstrate that it may be possible to provide power system control and operation algorithms with a real-time estimate of modal frequency and damping.
345 citations
TL;DR: In this paper, a spectral analysis of the two-color electronic distance measuring networks in California has been performed and it is shown that the noise power spectra are dominated by white noise at higher frequencies and power law behavior at lower frequencies.
Abstract: Analysis of frequent trilateration observations from the two-color electronic distance measuring networks in California demonstrate that the noise power spectra are dominated by white noise at higher frequencies and power law behavior at lower frequencies. In contrast, Earth scientists typically have assumed that only white noise is present in a geodetic time series, since a combination of infrequent measurements and low precision usually preclude identifying the time-correlated signature in such data. After removing a linear trend from the two-color data, it becomes evident that there are primarily two recognizable types of time-correlated noise present in the residuals. The first type is a seasonal variation in displacement which is probably a result of measuring to shallow surface monuments installed in clayey soil which responds to seasonally occurring rainfall; this noise is significant only for a small fraction of the sites analyzed. The second type of correlated noise becomes evident only after spectral analysis of line length changes and shows a functional relation at long periods between power and frequency of 1/ƒα, where ƒ is frequency and α≈2. With α=2, this type of correlated noise is termed random-walk noise, and its source is mainly thought to be small random motions of geodetic monuments with respect to the Earth's crust, though other sources are possible. Because the line length changes in the two-color networks are measured at irregular intervals, power spectral techniques cannot reliably estimate the level of 1/ƒα noise. Rather, we also use here a maximum likelihood estimation technique which assumes that there are only two sources of noise in the residual time series (white noise and random-walk noise) and estimates the amount of each. From this analysis we find that the random-walk noise level averages about 1.3 mm/√yr and that our estimates of the white noise component confirm theoretical limitations of the measurement technique. In addition, the seasonal noise can be as large as 3 mm in amplitude but typically is less than 0.5 mm. Because of the presence of random-walk noise in these time series, modeling and interpretation of the geodetic data must account for this source of error. By way of example we show that estimating the time-varying strain tensor (a form of spatial averaging) from geodetic data having both random-walk and white noise error components results in seemingly significant variations in the rate of strain accumulation; spatial averaging does reduce the size of both noise components but not their relative influence on the resulting strain accumulation model.
342 citations
01 Jan 1997
TL;DR: The generalized Langevin equation as discussed by the authors describes processes in a mechanical system with both deterministic and random forces which have comparable magnitudes (i.e., neither the deterministic nor random part can be neglected) and the random force is a transformed white noise.
Abstract: The generalized Langevin equation describes processes in a mechanical system with both deterministic and random forces which have comparable magnitudes (i.e., neither the deterministic nor random part can be neglected) and the random force is a transformed white noise. Examples of such processes are well known in physics. In this chapter, we use integral operators with Riemannian parallel translation in order to study the Langevin equations arising in geometric mechanics. Note that in the case under consideration the trajectories of the process are a.s. smooth. This makes the analysis of such systems technically much simpler than that of the general ones studied in Chap. 4.
314 citations
TL;DR: It is shown that the classical prediction problem must be reformulated when the relation function is taken into consideration, and this leads to a new perspective concerning the concept of complex white noise as well as the modeling of any signal as the output of a linear system driven by a white noise.
Abstract: The second-order statistical properties of complex signals are usually characterized by the covariance function. However, this is not sufficient for a complete second-order description, and it is necessary to introduce another moment called the relation function. Its properties, and especially the conditions that it must satisfy, are analyzed both for stationary and nonstationary signals. This leads to a new perspective concerning the concept of complex white noise as well as the modeling of any signal as the output of a linear system driven by a white noise. Finally, this is applied to complex autoregressive signals, and it is shown that the classical prediction problem must be reformulated when the relation function is taken into consideration.
256 citations
TL;DR: This paper investigates the application of the EM algorithm to sequence estimation in the presence of random disturbances and additive white Gaussian noise, and shows that a formulation of the sequence estimation problem can provide a means of obtaining ML sequence estimates.
Abstract: The expectation-maximization (EM) algorithm was first introduced in the statistics literature as an iterative procedure that under some conditions produces maximum-likelihood (hit) parameter estimates. In this paper we investigate the application of the EM algorithm to sequence estimation in the presence of random disturbances and additive white Gaussian noise. As examples of the use of the EM algorithm, we look at the random-phase and fading channels, and show that a formulation of the sequence estimation problem based on the EM algorithm can provide a means of obtaining ML sequence estimates, a task that has been previously too complex to perform.
248 citations
TL;DR: In this paper, an asymptotic approximation for evaluating the probability integrals that arise in the determination of the reliability and response moments of uncertain dynamic systems subject to stochastic excitation is developed.
Abstract: An asymptotic approximation is developed for evaluating the probability integrals that arise in the determination of the reliability and response moments of uncertain dynamic systems subject to stochastic excitation. The method is applicable when the probabilities of failure or response moments conditional on the system parameters are available, and the effect of the uncertainty in the system parameters is to be investigated. In particular, a simple analytical formula for the probability of failure of the system is derived and compared to some existing approximations, including an asymptotic approximation based on second-order reliability methods. Simple analytical formulas are also derived for the sensitivity of the failure probability and response moments to variations in parameters of interest. Conditions for which the proposed asymptotic expansion is expected to be accurate are presented. Since numerical integration is only computationally feasible for investigating the accuracy of the proposed method for a small number of uncertain system parameters, simulation techniques are also used. A simple importance sampling method is shown to converge much more rapidly than straightforward Monte Carlo simulation. Simple structures subjected to white noise stochastic excitation are used to illustrate the accuracy of the proposed analytical approximation. Results from the computationally efficient perturbation method are also included for comparison. The results show that the asymptotic method gives acceptable approximations, even for systems with relatively large uncertainty, and in most cases, it outperforms the perturbation method.
230 citations
TL;DR: A solution to the problem of identifying multivariable finite dimensional linear time-invariant systems from noisy input/output measurements is developed in the framework of subspace identification and it is shown that the proposed algorithms give consistent estimates when the system is operating in open- or closed-loop.
03 Nov 1997
TL;DR: A novel approach to loading for discrete multitone modulation (DMT) systems is proposed, which assigns energy to different subchannels in order to maximize the data rate for a given margin while previous algorithms proposed are aimed mainly at maximizing the margin at a target data rate.
Abstract: A novel approach to loading for discrete multitone modulation (DMT) systems is proposed. This algorithm assigns energy to different subchannels in order to maximize the data rate for a given margin while previous algorithms proposed are aimed mainly at maximizing the margin at a target data rate. Two implementations are suggested, both with finite granularity: the first one leads to the optimal waterfilling solution, and the second results in a suboptimal but slightly less complex flat-energy distribution. The algorithm is extended to the case of a rate adaptive system with both a target guaranteed fixed data rate service and a variable one. Simulation results are presented for a Rayleigh fading channel with additive white Gaussian noise.
TL;DR: In this article, an integrated noise source (INS) was fabricated in a standard 1.2 /spl mu/m digital CMOS technology, which was coupled into a comparator to generate a random digital bit stream.
Abstract: An integrated noise source (INS) has been fabricated in a standard 1.2 /spl mu/m digital CMOS technology. Wideband white noise is generated from the amplified thermal noise of large resistors, which in turn is coupled into a comparator to generate a random digital bit stream. The INS generates 100 mV rms of analog output noise over a bandwidth of 3.2 MHz and operates from a single 5 V power supply with a quiescent current of 7.4 mA. The circuit has an area of 2.92 mm/sup 2/. Potential applications of the INS include data encryption, mathematical simulation, and circuit test and measurement.
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TL;DR: A partial response system of the polynomial (1+D)(1-D 2) called modified E 2 PR4(MEEPR4) exhibits the best performance and is considered the best combination of (1,7) RLL code and a partial response channel.
Abstract: The areal density of magnetic disk is expected to grow to over 20Gb/in 2 by the end-1990s. In the INTERMAG'96, the authors demonstrated high areal density of 5Gbit/in 2 with PR4ML channel. The thermal instability can not be neglected at higher areal density than 5Gb/in 2 . It indicates the necessity to use the code which expand bit length such as (1,7) RLL code. We consider the best combination of (1,7) RLL code and a partial response channel. The partial response system of the polynomial (1+D)(1-D 2 ) called modified E 2 PR4(MEEPR4) exhibits the best performance. We performed this partial response simulation model with white noise and media - noise and examined the architectural consideration in the design of the experimental prototype.
TL;DR: In this article, a coordinatewise thresholded wavelet estimator based on a tensor product basis with separate scale parameter for every dimension is proposed for estimation in anisotropic smoothness classes.
Abstract: We derive minimax rates for estimation in anisotropic smoothness classes. This rate is attained by a coordinatewise thresholded wavelet estimator based on a tensor product basis with separate scale parameter for every dimension. It is shown that this basis is superior to its one-scale multiresolution analog, if different degrees of smoothness in different directions are present.; As an important application we introduce a new adaptive wavelet estimator of the time-dependent spectrum of a locally stationary time series. Using this model which was resently developed by Dahlhaus, we show that the resulting estimator attains nearly the rate, which is optimal in Gaussian white noise, simultaneously over a wide range of smoothness classes. Moreover, by our new approach we overcome the difficulty of how to choose the right amount of smoothing, i.e. how to adapt to the appropriate resolution, for reconstructing the local structure of the evolutionary spectrum in the time-frequency plane.
TL;DR: The transfer function enabled us to accurately predict acoustic noise output for a pulse sequence consisting of a series of trapezoidal pulses on a single axis and for a clinical fast spin echo sequence with gradients present on all three axes.
Abstract: Gradient acoustic noise has been measured and characterized for an epoxy-potted, shielded gradient assembly in a 1.5 T MRI system. Noise levels vary by 10 dB or more as a function of longitudinal position in the scanner and reflect the pattern of forces applied to the gradient assembly. The noise level increases slightly (1-3 dB) with a patient in the scanner. The spectrum of the noise is similar (but not identical) to the spectrum of the input signal. A gradient-pulse-to-acoustic-noise transfer function was obtained by using a white noise voltage input to the gradient system. The transfer function enabled us to accurately predict acoustic noise output for a pulse sequence consisting of a series of trapezoidal pulses on a single axis and for a clinical fast spin echo sequence with gradients present on all three axes.
TL;DR: In this paper, the authors proposed a method for frequency estimation in a power system by demodulation of two complex signals, which does not introduce a double frequency component and can improve fast frequency estimation of signals with good noise properties.
Abstract: This paper presents a method for frequency estimation in a power system by demodulation of two complex signals. In power system analysis, the /spl alpha//spl beta/-transform is used to convert three phase quantities to a complex quantity where the real part is the in-phase component and the imaginary part is the quadrature component. This complex signal is demodulated with a known complex phasor rotating in opposite direction to the input. The advantage of this method is that the demodulation does not introduce a double frequency component. For signals with high signal to noise ratio, the filtering demand for the double frequency component can often limit the speed of the frequency estimator. Hence, the method can improve fast frequency estimation of signals with good noise properties. The method loses its benefits for noisy signals, where the filter design is governed by the demand to filter harmonics and white noise. The method has been previously published, but not explored to its potential. The paper presents four examples to illustrate the strengths and weaknesses of the method.
TL;DR: In this article, the authors apply state-of-the art data analysis methods to a number of fictitious cosmic microwave background (CMB) mapping experiments, including $1/f$ noise, distilling the cosmological information from time-ordered data to maps to power spectrum estimates, and find that in all cases the resulting error bars can be well approximated by simple and intuitive analytic expressions.
Abstract: We apply state-of-the art data analysis methods to a number of fictitious cosmic microwave background (CMB) mapping experiments, including $1/f$ noise, distilling the cosmological information from time-ordered data to maps to power spectrum estimates, and find that in all cases the resulting error bars can be well approximated by simple and intuitive analytic expressions. Using these approximations, we discuss how to maximize the scientific return of CMB mapping experiments given the practical constraints at hand, and our main conclusions are as follows. (1) For a given resolution and sensitivity, it is best to cover a sky area such that the signal-to-noise ratio per resolution element (pixel) is of order unity. (2) It is best to avoid excessively skinny observing regions, narrower than a few degrees. (3) The minimum-variance map-making method can reduce the effects of $1/f$ noise by a substantial factor, but only if the scan pattern is thoroughly interconnected. (4) $1/f$ noise produces a $1/\mathcal{l}$ contribution to the angular power spectrum for well-connected single-beam scanning, as compared to virtually white noise for a two-beam scan pattern such as that of the MAP satellite.
TL;DR: It is shown that decryption transforms the noise added to the encrypted image to a wide-sense stationary additive white noise, which enables easy implementation and data compression while recovering images of good quality may be possible.
Abstract: We investigate the robustness of an image encryption technique that uses random phase encoding in both the input plane and the Fourier plane when the encrypted image has been distorted. The distortions include different types of noise, loss of encrypted data, and the binarization of the encrypted image. It is shown that decryption transforms the noise added to the encrypted image to a wide-sense stationary additive white noise. Consequently, regardless of the type of the noise added to the encrypted image, the quality of the recovered image can be improved by processing techniques designed for filtering out additive white noise. The double-phase encryption technique distributes the input image over the entire output plane, which provides robustness to the distortions due to loss of encrypted data. Regarding the binarization, it enables easy implementation and data compression while recovering images of good quality may be possible.
01 Jan 1997
TL;DR: In this paper, the authors developed methods for unequally spaced data using stochastic partial differential equations driven by white noise to describe continuous spatial temporal processes in one or two spatial dimensions and time.
Abstract: Existing statistical methodologies for space-time processes are usually developed for equally spaced data This paper develops methods for unequally spaced data Stochastic partial differential equations driven by white noise are used to describe continuous spatial temporal processes The models considered here are based on the heat or diffusion equation in one or two spatial dimensions and time Spectral density functions of these processes can be calculated and inverted using Fourier transforms to obtain the corresponding covariance functions In two spatial dimensions, numerical integrations are necessary for calculating the correlation functions for these models since Hankel transforms without closed form solutions are involved FORTRAN subroutines are available to calculate these Hankel transforms and covariance functions Maximum likelihood estimation can be used assuming Gaussian errors For small data sets (up to several hundred observations) exact maximum likelihood estimates can be calculated directly For larger data sets, nearest neighbor methods can be used to reduce the computation and obtain approximate likelihoods Two examples are discussed
TL;DR: The proposed method for the detection and parameter estimation of mono or multicomponent polynomial-phase signals embedded in white Gaussian noise and based on a generalized ambiguity function is shown to be asymptotically efficient for second-order PPS and nearly asymptic efficient for third- order PPSs.
Abstract: The aim of this work is the performance analysis of a method for the detection and parameter estimation of mono or multicomponent polynomial-phase signals (PPS) embedded in white Gaussian noise and based on a generalized ambiguity function. The proposed method is shown to be asymptotically efficient for second-order PPS and nearly asymptotically efficient for third-order PPSs. The method presents some advantages with respect to similar techniques, like the polynomial-phase transform, for example, in terms of (i) a closer approach to the Cramer-Rao lower bounds, (ii) a lower SNR threshold, (iii) a better capability of discriminating multicomponent signals.
TL;DR: In this paper, a contrast gain control mechanism that pools activity across spatial frequency, orientation and space to inhibit (divisively) the response of the receptor sensitive to the signal was proposed.
Abstract: Studies of visual detection of a signal superimposed on one of two identical backgrounds show performance degradation when the background has high contrast and is similar in spatial frequency and/or orientation to the signal. To account for this finding, models include a contrast gain control mechanism that pools activity across spatial frequency, orientation and space to inhibit (divisively) the response of the receptor sensitive to the signal. In tasks in which the observer has to detect a known signal added to one of M different backgrounds grounds due to added visual noise, the main sources of degradation are the stochastic noise in the image and the suboptimal visual processing. We investigate how these two sources of degradation (contrast gain control and variations in the background) interact in a task in which the signal is embedded in one of M locations in a complex spatially varying background (structured background). We use backgrounds extracted from patient digital medical images. To isolate effects of the fixed deterministic background (the contrast gain control) from the effects of the background variations, we conduct detection experiments with three different background conditions: (1) uniform background, (2) a repeated sample of structured background, and (3) different samples of structured background. Results show that human visual detection degrades from the uniform background condition to the repeated background condition and degrades even further in the different backgrounds condition. These results suggest that both the contrast gain control mechanism and the background random variations degrade human performance in detection of a signal in a complex, spatially varying background. A filter model and added white noise are used to generate estimates of sampling efficiencies, an equivalent internal noise, an equivalent contrast-gain-control-induced noise, and an equivalent noise due to the variations in the structured background.
TL;DR: A detector of a spatially distributed target in white Gaussian noise using a simple detector form, whose detection performance is robust over different scattering densities.
Abstract: A detector of a spatially distributed target in white Gaussian noise is developed. A reasonable distribution for the a priori target scatterer density is assumed, and a detector that incorporates this a priori knowledge is given. A simple detector form results, whose detection performance is robust over different scattering densities.
TL;DR: The main advantage of this technique over previous methods is that it is able to compute complicated motions without the necessity of integrating dynamical equations over time, and a user can view and manipulate tree‐motions in real‐time.
Abstract: This paper addresses the problem of realistically simulating the motion of tree-branches subjected to turbulence. Since the resulting motion is random in nature, we model it as a stochastic process. We synthesize this process directly by filtering a white noise in the Fourier domain. The filter is constructed by performing a modal analysis of the tree. We use a sophisticated numerical technique which is able to compute the first few significant modes of large trees. The main advantage of our technique over previous methods is that we are able to compute complicated motions without the necessity of integrating dynamical equations over time. Consequently, a user can view and manipulate tree-motions in real-time Our technique can be further extended to other flexible structures such as two-dimensional plates.
TL;DR: A real-time predictive e lter is derived for nonlinear systems that determines the optimal model error and provides accurate estimates in the presence of highly nonlinear dynamics and signie cant errors in the model parameters.
Abstract: A real-time predictive e lter is derived for nonlinear systems. The major advantage of this new e lter over conventional e lters is that it providesa method of determining optimalstate estimatesin the presenceof signie cant error in the assumed (nominal)model. The new real-time nonlinear e lter determines (predicts)the optimal model errortrajectorysothatthemeasurement-minus-estimatecovariancestatisticallymatchestheknownmeasurement- minus-truth covariance. The optimal model error is found by using a one-time step ahead control approach. Also, because the continuous model is used to determine state estimates, the e lter avoids discrete state jumps. The predictive e lter is used to estimate the position and velocity of nonlinear mass-damper-spring system. Results using this new algorithm indicate that the real-time predictive e lter provides accurate estimates in the presence of highly nonlinear dynamics and signie cant errors in the model parameters. ONVENTIONAL e lter methods, such as the Kalman e lter, 1 have proven to be extremely useful in a wide range of appli- cations, including noise reduction of signals, trajectory tracking of moving objects, and control of linear or nonlinear systems. The es- sential feature of the Kalman e lter is the utilization of state-space formulations for the system model. Errors in the dynamics system can be separated into process noise errors or modeling errors. Pro- cess noise errors are usually represented by a zero-mean Gaussian errorprocesswithknowncovariance (e.g.,agyro-errormodelcanbe represented by a random walk process ). Modeling errors are usu- ally not known explicitly, because system models are not usually improved or updated during the estimation process. The theoretical derivation of the expression for the estimate error covariance in the Kalman e lter is only available if one makes assumptions about the model error. The most common assumptions about the model error are that it is also a zero-mean Gaussian noise process. Therefore, in the e lter-type literature, most often process noise and model error are treated equally. The Kalman e lter satise es an optimality criterion, which min- imizes the trace of the covariance of the estimate error between the system model responses and actual measurements. Statistical properties of the process noise and measurement error are used to determine an optimal e lter design. Therefore, model characteristics are combined with sequential measurements in order to obtain state estimates that are more accurate than both the measurements and model responses. As already stated, errors in the system model of the Kalman e l- ter are usually assumed to be represented by a zero-mean Gaussian noise process with known covariance. In actual practice the noise covariance is usually determined by an ad hoc and/or heuristic es- timation approach, which may result in suboptimal e lter designs. Otherapplicationsalsodetermineasteady-stategaindirectly,which may even produce unstable e lterdesigns. 2 Also, in many cases such as nonlinearities in the actual system responses or nonstationary processes, the assumption of a Gaussian model error process can lead to severely degraded state estimates.
04 May 1997
TL;DR: In this article, the TLS-ESPRIT is applied to estimate the unknown frequencies of sinusoids in a white nonstationary noise, and it is shown that if the noise (after the deconvolution) is a white stationary process, then TLS-EPRIT derived returns to TLS-EsPRIT for the stationary noise case, and the computer simulations show that the derived method produces unbiased, high resolution time delay estimates.
Abstract: In this paper TLS-ESPRIT is applied to a time delay estimation. The correlation method and a frequency domain deconvolution are used to convert the time delay estimation problem to a estimation of frequencies of complex sinusoids in a white nonstationary noise. TLS-ESPRIT is then applied to estimate the unknown frequencies, i.e., the time delays. It is shown that if the noise (after the deconvolution) is a white stationary process the TLS-ESPRIT derived returns to TLS-ESPRIT derived earlier for the stationary noise case. The computer simulations show that the derived method produces unbiased, high resolution time delay estimates.
TL;DR: The effect of red, white and blue environmental noise on discrete–time population dynamics is analysed and the power spectra of the population dynamics with noise are red in stable, periodic and aperiodic ranges irrespective of the noise colour.
Abstract: The effect of red, white and blue environmental noise on discrete-time population dynamics is analyzed. The coloured noise is superimposed on Moran-Ricker and Maynard Smith dynamics, the resulting power spectra are less than examined. Time series dominated by short- and long-term fluctuations are said to be blue and red, respectively. In the stable range of the Moran-Ricker dynamics, environmental noise of any colour will make population dynamics red or blue depending the intrinsic growth rate. Thus, telling apart the colour of the noise from the colour of the population dynamics may not be possible. Population dynamics subjected to red and blue environmental noises show, respectively, more red or blue power spectra than those subjected to white noise. The sensitivity to differences in the noise colours decreases with increasing complexity and ultimately disappears in the chaotic range of the population dynamics. These findings are duplicated with the Maynard Smith model for high growth rates when the strength of density dependence changes. However, for low growth rates the power spectra of the population dynamics with noise are red in stable, periodic and aperiodic ranges irrespective of the noise colour. Since chaotic population fluctuations may show blue spectra in the deterministic case, this implies that blue deterministic chaos may become red under any colour of the noise.
TL;DR: The development of an improved 2-D adaptive lattice algorithm (2-D AL) and its application to the removal of correlated clutter to enhance the detectability of small objects in images is focused on.
Abstract: Two-dimensional (2-D) adaptive filtering is a technique that can be applied to many image processing applications. This paper will focus on the development of an improved 2-D adaptive lattice algorithm (2-D AL) and its application to the removal of correlated clutter to enhance the detectability of small objects in images. The two improvements proposed here are increased flexibility in the calculation of the reflection coefficients and a 2-D method to update the correlations used in the 2-D AL algorithm. The 2-D AL algorithm is shown to predict correlated clutter in image data and the resulting filter is compared with an ideal Wiener-Hopf filter. The results of the clutter removal will be compared to previously published ones for a 2-D least mean square (LMS) algorithm. 2-D AL is better able to predict spatially varying clutter than the 2-D LMS algorithm, since it converges faster to new image properties. Examples of these improvements are shown for a spatially varying 2-D sinusoid in white noise and simulated clouds. The 2-D LMS and 2-D AL algorithms are also shown to enhance a mammogram image for the detection of small microcalcifications and stellate lesions.
16 Apr 1997
TL;DR: A method for estimating the parameters of frequency-hopping signals embedded in noise based on representation of the signal in the time-frequency domain and integration along paths expressed in a parametric form depending on the parametric law characterizing the signal instantaneous frequency.
Abstract: In this paper we propose a method for estimating the parameters of frequency-hopping (FH) signals embedded in noise. The method is based on two main steps: (i) representation of the signal in the time-frequency domain; (ii) integration in the time-frequency domain along paths expressed in a parametric form depending on the parametric law characterizing the signal instantaneous frequency. The method does not make any assumption about the alphabet of hopping frequencies, the duration of each hop, or the synchronization. The performance of the method is given in terms of estimation variances.
21 Apr 1997
TL;DR: Experimental results demonstrate that the speech enhancement algorithm using the wavelet transform is very promising and to prevent the quality degradation of the unvoiced sounds during the denoising process.
Abstract: This paper describes a general problem of removing additive background noise from the noisy speech in the wavelet domain. A semisoft thresholding is used to remove noise components from the wavelet coefficients of noisy speech. To prevent the quality degradation of the unvoiced sounds during the denoising process, the unvoiced region is classified first and then thresholding is applied in a different way. Experimental results demonstrate that the speech enhancement algorithm using the wavelet transform is very promising.