Showing papers on "White noise published in 1989"
TL;DR: In this article, the authors analyzed the performance of Root-Music, a variation of the MUSIC algorithm, for estimating the direction of arrival (DOA) of plane waves in white noise in the case of a linear equispaced sensor array.
Abstract: The authors analyze the performance of Root-Music, a variation of the MUSIC algorithm, for estimating the direction of arrival (DOA) of plane waves in white noise in the case of a linear equispaced sensor array The performance of the method is analyzed by examining the perturbation in the roots of the polynomial formed in the intermediate step of Root-Music In particular, asymptotic results for the mean squared error in the estimates of the direction of arrival are derived Simplified expressions are presented for the one- and two-source case and compared to those obtained for least-squares ESPRIT Computer simulations are also presented, and they are in close agreement with the theory An important outcome of this analysis is the fact that the error in the signal zeros has a largely radial component This provides an explanation as to why the Root-Music is superior to the spectral MUSIC algorithm >
854 citations
TL;DR: The authors model a finite-dimensional system as an ARMA (autoregressive moving-average) rational function of known orders, but the special cases of AR, MA, and all-pass models are also considered.
Abstract: A method is presented for identification of linear, time-variant, nonminimum phase systems when only output data are available. The input sequence need not be independent, but it must be non-Gaussian, with some special properties described in the test. The authors model a finite-dimensional system as an ARMA (autoregressive moving-average) rational function of known orders, but the special cases of AR, MA, and all-pass models are also considered. To estimate the parameters of their model, the authors utilize both second- and higher-order statistics of the output, which may be contaminated by additive, zero-mean, Gaussian white noise of unknown variance. The parameter estimators obtained are proved, under mild conditions, to be consistent. Simulations verify the performance of the proposed method in the case of relatively low signal-to-noise ratios, and when there is a model-order mismatch. >
492 citations
TL;DR: The Burgers equation is the simplest nonlinear generalization of the diffusion equation subject to random noise and it is shown that an exponent identity observed in all simulations so far follows simply from the Galilean invariance of the equation in the absence of temporal correlations.
Abstract: The Burgers equation is the simplest nonlinear generalization of the diffusion equation. We present a detailed dynamical renormalization-group analysis of this equation subject to random noise. The noise itself can be the product of another stochastic process and is hence allowed to have correlations in space and/or time. In dimensions higher than a critical ${d}_{c}$ weak and strong noise lead to different scaling exponents, while for d${d}_{c}$ any amount of noise is relevant resulting in strong-coupling behavior. In the absence of temporal correlations we find two regimes for d${d}_{c}$: either the hydrodynamic behavior is determined by white noise and correlations are unimportant, or correlations dominate and the resulting scaling exponents can be obtained exactly. With temporal correlations present, the hydrodynamic behavior is much more complex, as renormalization predicts a complicated dependence of the effective noise spectrum on frequency in certain regimes. The relevance of these results to two interesting problems is discussed. One is the anomalous transverse fluctuations of a directed polymer in a random medium, and the other is a description of a growing interface. Various recent numerical simulations are reviewed in the light of these results. For example, we show that an exponent identity observed in all simulations so far follows simply from the Galilean invariance of the equation in the absence of temporal correlations.
390 citations
26 Jun 1989
TL;DR: In this paper, the switching pattern can be randomized by modulating the triangle carrier in sinusoidal PWM (pulse-width modulation) with bandlimited white noise, which can be used to avoid the concentration of harmonic energy in distinct tones.
Abstract: Acoustic noise in an inverter-driven AC electric machine can be reduced by avoiding the concentration of harmonic energy in distinct tones. One method to spread out the harmonic spectrum without the use of programmed PWM (pulse-width modulation) is to cause the switching pattern to be random. It is proposed that the switching pattern can be randomized by modulating the triangle carrier in sinusoidal PWM (pulse-width modulation) with bandlimited white noise. All the advantages of sinusoidal PWM are preserved with this technique. These include real-time control, linear operation, good transient response, and a constant average switching frequency. By controlling the bandwidth and RMS value of the pink noise modulation, it is shown that the instantaneous variation in switching frequency as well as the bandwidth of the energy spectrum in the machine can be specified within predetermined limits. Experimental results show the absence of acoustic noise concentrated at specific tones which is present with conventional sinusoidal modulation. >
290 citations
TL;DR: It is shown that for small changes around the optimum solution, the proposed update algorithm approximates the Gauss-Newton update without requiring a matrix inversion, and the transient response is approximately independent of the number of sinusoids or their power levels.
Abstract: A constrained adaptive infinite-impulse-response (IIR) filter consisting of a cascade of biquadratic notch sections is used to track multiple sinusoids. The structure can be used to isolate individual sinusoids. It is shown that for small changes around the optimum solution, the proposed update algorithm approximates the Gauss-Newton update without requiring a matrix inversion. Furthermore, the transient response is approximately independent of the number of sinusoids or their power levels. Important adaptive line enhancement performance criteria, such as signal-to-noise improvement ratio and the bias in the frequency estimate assuming white noise, are derived for the single-sinusoid case. Computer simulations are used to demonstrate the performance of the notch filter under a wide range of conditions. >
256 citations
TL;DR: It is shown that the sample mean and power of the binary quantization noise are consistent with the common uniform distribution assumption, but that the autocorrelation and power spectrum are not consistent withThe white noise assumption.
Abstract: An exact discrete-time analysis of the moments and spectra of the quantization noise of a discrete-time single-loop sigma-delta modulator with a DC input is presented. An exact difference equation for the discrete-time nonlinear system is used to evaluate the first- and second-order moments and power spectrum of the binary quantizer noise and the binary quantizer output for a single-loop sigma-delta encoder with a DC input. It is shown that the sample mean and power of the binary quantization noise are consistent with the common uniform distribution assumption, but that the autocorrelation and power spectrum are not consistent with the white noise assumption. The results are used to evaluate the overall sample average mean squared quantization error as a function of the decimation filter used. >
203 citations
TL;DR: The authors evaluate two subspace-based methods, ESPRIT and the Toplitz approximation method (TAM), for estimating the direction of arrival (DOA) of plane waves in white noise in the case of linear equispaced sensor array and it is shown that the least-squares versions of ESPRit and TAM result in the same estimate and are statistically equivalent.
Abstract: The authors evaluate two subspace-based methods, ESPRIT and the Toplitz approximation method (TAM), for estimating the direction of arrival (DOA) of plane waves in white noise in the case of linear equispaced sensor array. It is shown that the least-squares versions of ESPRIT and TAM result in the same estimate and are statistically equivalent. It is also shown that, asymptotically, the estimates obtained using least-squares ESPRIT and total-least-squares ESPRIT have the same mean-squared error. Expressions for the asymptotic mean-squared error in the estimates of the DOA are derived for both methods. Simple closed-form expressions are derived for the one- and two-source cases to get further insight. Computer simulation results are provided to substantiate the analysis. >
181 citations
TL;DR: The proposed detector is capable of separating transients having different arrival times, even in this case where their waveforms partially overlap.
Abstract: Gabor representation is used for the detection of transient signals with unknown arrival times. A one-sided exponential window function is used which seems to be most appropriate for transient modelling. Explicit expressions for the Gabor coefficients are given for this window function. When the given signal is random, so are the coefficients. The second-order moments of the Gabor coefficients are computed for a white noise signal. These are then used to introduce a detection statistic based on the Gabor coefficients. The proposed detector is capable of separating transients having different arrival times, even in this case where their waveforms partially overlap. >
177 citations
TL;DR: In this paper, a 5-MHz residual linewidth is observed in the high-power limit of 1.3 mu m DFB (distributed feedback) semiconductor lasers.
Abstract: The FM-noise spectrum and the linewidth of 1.3 mu m DFB (distributed feedback) semiconductor lasers measured in the high-power state up to 20 mW are discussed. A 5-MHz residual linewidth is observed in the high-power limit. The FM-noise spectrum consists of white noise and 1/f noise. The spectral density of the white noise is reduced by the increase in the output power, whereas that of the 1/f noise is unchanged, which means that the linewidth residual in the high-power limit is caused by the 1/f noise rather than the white noise. The impact of the 1/f-type FM noise on coherent optical communication systems is also discussed. >
175 citations
TL;DR: It is found that the orientation (or direction) of the cepstral vector is less susceptible to noise perturbation than the vector norm, and a family of distortion measures based on the projection between two cEPstral vectors is proposed, which have the same computational efficiency as the band-pass cepStral distortion measure.
Abstract: Consideration is given to the formulation of speech similarity measures, a fundamental component in recognizer designs, that are robust to the change of ambient conditions. The authors focus on the speech cepstrum derived from linear prediction coefficients (the LPC cepstrum). By using some common models for noisy speech, they show analytically that additive white noise reduces the norm (length) of the LPC cepstral vectors. Empirical observations on the parameter histograms not only confirm the analytical results through the use of noise models but further reveal that at a given (global) signal-to-noise ratio (SNR), the norm reduction on cepstral vectors with larger norms is generally less than on vectors with smaller norms, and that lower order coefficients are more affected than higher order terms. In addition, it is found that the orientation (or direction) of the cepstral vector is less susceptible to noise perturbation than the vector norm. As a consequence of the above results, a family of distortion measures based on the projection between two cepstral vectors is proposed. The new measures have the same computational efficiency as the band-pass cepstral distortion measure. >
166 citations
TL;DR: 1/f noise is tied directly to a probability limit distribution and a second mechanism involving scaling is introduced to provide a natural crossover from log-normal to inverse power-law behavior and generates 1/fα noise instead of pure 1/ f noise.
Abstract: A generic mechanism for the ubiquitous phenomenon of 1/f noise is reviewed. This mechanism arises in random processes expressible as a product of several random variables. Under mild conditions this product form leads to the log-normal distribution which we show straightforwardly generates 1/f noise. Thus, 1/f noise is tied directly to a probability limit distribution. A second mechanism involving scaling is introduced to provide a natural crossover from log-normal to inverse power-law behavior and generates 1/fα noise instead of pure 1/f noise. Examples of these distributions and the transitions between them are drawn from such diverse areas as economics, scientific productivity, bronchial structure and cardiac activity.
TL;DR: In this article, the authors developed the theory of CA-CFAR (cell-averaging constant false-alarm rate) detection using multiple sensors and data fusion, where detection decisions are transmitted from each CA -CFAR detector to the data fusion center.
Abstract: The authors develop the theory of CA-CFAR (cell-averaging constant false-alarm rate) detection using multiple sensors and data fusion, where detection decisions are transmitted from each CA-CFAR detector to the data fusion center. The overall decision is obtained at the data fusion center based on some k out of n fusion rule. For a Swerling target model I embedded in white Gaussian noise of unknown level, the authors obtain the optimum threshold multipliers of the individual detectors. At the data fusion center, they derive an expression for the overall probability of detection while the overall probability of false alarm is maintained at the desired value for the given fusion rules. An example is presented showing numerical results. >
23 May 1989
TL;DR: The colored-noise prefilter greatly enhances the quality and intelligibility of LPC output speech for noisy inputs, and it is demonstrated that such gains are unavailable with white noise assumption Kalman and Wiener filters.
Abstract: A report is presented on experiments using a colored-noise assumption Kalman filter to enhance speech additively contaminated by colored noise, such as helicopter noise and jeep noise, with a particular application to linear predictive coding (LPC) of noisy speech. The results indicate that the colored-noise Kalman filter provides a significant gain in SNR, a clear improvement in the sound spectrogram, and an audible improvement in output speech quality. The authors demonstrate that such gains are unavailable with white noise assumption Kalman and Wiener filters. The colored-noise prefilter greatly enhances the quality and intelligibility of LPC output speech for noisy inputs. >
TL;DR: An asymptotic analysis is presented of a class of high-resolution estimators for resolving correlated and coherent plane waves in noise and the variance of the conventional MUSIC estimator along the true arrival angles is shown to be zero within a first-order approximation.
Abstract: An asymptotic analysis is presented of a class of high-resolution estimators for resolving correlated and coherent plane waves in noise These estimators are in turn constructed from certain eigenvectors associated with spatially smoothed (or unsmoothed) covariance matrices generated from a uniform array The analysis is first carried out for the smoothed case, and from this the conventional (ie, unsmoothed) multiple signal classification (MUSIC) scheme follows as a special case Independent of the total number of sources present in the scene, the variance of the conventional MUSIC estimator along the true arrival angles is shown to be zero within a first-order approximation The bias expressions in the smoothed case are used to obtain a resolution threshold for two coherent, equipowered plane waves in white noise, and the result is compared to the one derived by Kaveh et al (1986) for two uncorrelated, equipowered plane waves >
TL;DR: Initial implementation of the SMC in a speaker-dependent isolated word recognizer shows an improvement in recognition accuracy equivalent to an increase in input SNR of approximately 13 dB, as compared to the LPC recognizer.
Abstract: A technique for robust spectral representation of all-pole sequences is proposed. It is shown that the autocorrelation of an all-pole sequence, obtained by passing white noise through an all-pole filter 1/A(z), is an all-pole sequence of the form 1/A/sup 2/(z). A short-time modified coherence (SMC) representation is proposed that is an all-pole modeling of the autocorrelation sequence with a spectral shaper. The spectral shaper, essentially a square root operator in the frequency domain, compensates for the inherent spectral distortion introduced by the autocorrelation operation on the autocorrelation sequence of the signal. The properties of the SMC representation, especially its robustness to additive white noise, are analyzed. Initial implementation of the SMC in a speaker-dependent isolated word recognizer shows an improvement in recognition accuracy equivalent to an increase in input SNR of approximately 13 dB, as compared to the LPC recognizer. >
Patent•
24 May 1989TL;DR: In this paper, the pseudo-random-bit sequence generators are clocked by separate clock signals of different frequency, which are logically combined by an exclusive-or gate to provide a pseudo random bit sequence generator which, when converted to analog signal has improved white noise characteristics.
Abstract: The pseudo-random-bit sequence generators are clocked by separate clock signals of different frequency. The two pseudo-random-bit sequence generator outputs are logically combined by an exclusive-or gate to provide a pseudo-random-bit sequence generator which, when converted to analog signal has improved white noise characteristics.
Book•
01 Jun 1989
TL;DR: A variety of applications of singular value decomposition in identification and signal processing and a novel method for reducing the computational load of SVD-based high discrimination algorithms.
Abstract: Parts: I. Tutorials. 1. Singular value decomposition: an introduction (P. Dewilde, E.F. Deprettere). 2. A variety of applications of singular value decomposition in identification and signal processing (J. Vandewalle, B. De Moor). 3. Eigen and singular value decomposition techniques for the solution of harmonic retrieval problems (M. Bouvet, H. Clergeot). 4. Advances in principal component signal processing (R.J. Vaccaro et al.). II: Model Reduction and Identification. 5. An overview of Hankel norm model reduction (A.C.M. Ran). 6. Identification of linear state space models with singular value decomposition using canonical correlation concepts (B. De Moor et al.). 7. Detection of multiple sinusoids in white noise: a signal enhancement approach (J.A. Cadzow et al.). III: Total Least Squares and GSVD. 8. The total least squares technique: computation, properties and applications (S. van Huffel, J. Vandewalle). 9. Oriented energy and oriented signal-to-signal ratio concepts in the analysis of vector sequences and time series (B. De Moor et al.). 10. ESPRIT - Estimation of signal parameters via rotational invariance techniques (R. Roy, T. Kailath). IV: Real-Time, Adaptive and Acceleration Algorithms. 11. On-line algorithm for signal separation based on SVD (D. Callaerts et al.). 12. A family of rank-one subspace updating methods (R.D. DeGroat, R.A. Roberts). 13. An array processing technique using the first principal component (P. Comon). 14. A novel method for reducing the computational load of SVD-based high discrimination algorithms (J.L. Mather). 15. Singular value decomposition of Frobenius Matrices for approximate and multi-objective signal processing tasks (E.A. Trachtenberg). V: Algorithms and Architectures. 16. On block Kogbetliantz methods for computation of the SVD (K.V. Fernando, S.J. Hammarling). 17. Reducing the number of sweeps in Hestenes' Method (P.C. Hansen). 18. Computational arrays for cyclic-by-rows Jacobi-algorithms (L. Thiele). 19. The symmetric tridiagonal eigenproblem on a custom linear array and hypercubes (E. de Doncker et al.). 20. Computing the singular value decomposition on the connection machine (L.M. Ewerbring, F.T. Luk). 21. Singular value decomposition on warp (M. Annaratone). 22. Execution of linear algebra operations on the SPRINT (A.J. De Groot et al.). VI. Resolution Limits, Enhancements and Questions. 23. An SVD analysis of resolution limits for harmonic retrieval problems (J.R. Casar, G. Cybenko). 24. A new application of SVD to harmonic retrieval (S. Mayrargue, J.P. Jouveau). 25. Retrieval of significant parameters from magnetic resonance signals via singular value decomposition (R. de Beer et al.).
TL;DR: The authors assume a state-variable model for the MD and generally obtain a nonlinear estimation problem with additional randomly varying system parameters such as received signal power, frequency offset, and Doppler spread.
Abstract: Unified modeling and estimation of the MD (multiplicative distortion) in finite-alphabet digital communication systems is presented. A simple form of MD is the carrier phase exp(j theta ), which has to be estimated and compensated for in a coherent receiver. A more general case with fading must, however, allow for amplitude as well as phase variations of the MD. The authors assume a state-variable model for the MD and generally obtain a nonlinear estimation problem with additional randomly varying system parameters such as received signal power, frequency offset, and Doppler spread. An extended Kalman filter is then applied as a near-optimal solution to the adaptive MD and channel parameter estimation problem. Examples are given to show the use and some advantages of this scheme. >
TL;DR: The authors study the effect of correlated noise on the performance of a distributed detection system and consider a suboptimal scheme by assuming that the local sensors have the same operating point, and that the distribution of the sensor observation is symmetric.
Abstract: The authors study the effect of correlated noise on the performance of a distributed detection system. They consider a suboptimal scheme by assuming that the local sensors have the same operating point, and that the distribution of the sensor observation is symmetric. This implies that the joint distribution of the sensor decisions, and therefore the fusion rule, are symmetric functions of the sensor decisions. The detection of a known signal in additive Gaussian noise and in Laplacian noise are considered. In both cases, system performance deteriorates when the correlation between the sensor noises is positive and increasing, whereas the performance improves considerably when the correlation is negative and increasing in magnitude. >
TL;DR: It is shown that the algorithms converge to the unknown characteristic in a pointwise manner and that the mean integrated square error converges to zero as the number of observations tends to infinity.
Abstract: The non-linearity in a discrete system governed by the Hammerstein functional is identified. The system is driven by a random while input signal and the output is disturbed by a random white noise. No parametric a priori information concerning the non-linearity is available and non-parametric algorithms are proposed. The algorithms are derived from the trigonometric as well as Hermite orthogonal series. It is shown that the algorithms converge to the unknown characteristic in a pointwise manner and that the mean integrated square error converges to zero as the number of observations tends to infinity. The rate of convergence is examined. A numerical example is also given.
TL;DR: A technique is presented for obtaining bounds on the average probability of error for direct-sequence spread-spectrum multiple-access (DS/SSMA) communications that yields arbitrarily right bounds, involves a small amount of computation, avoids numerical integrations, and applies to many types of detection.
Abstract: A technique is presented for obtaining bounds on the average probability of error for direct-sequence spread-spectrum multiple-access (DS/SSMA) communications. The technique is of interest because it yields arbitrarily right bounds, involves a small amount of computation, avoids numerical integrations, and applies to many types of detection. As an illustration, the technique is applied to binary DS/SSMA communications, an additive white Gaussian noise channel, and a coherent correlation receiver. It is assumed that all the signature sequences are deterministic. Each transmitter is assumed to have the same power, although the approach can accommodate the case of transmitters with unequal powers. Expressions are given for the density functions of the random variables that model the multiple-access interference. These expressions are used to obtain arbitrarily tight upper and lower bounds on the average probability of error without making a Gaussian approximation or performing numerical integrations to incorporate the effects of multiple-access interference. >
TL;DR: A simple linear procedure is given to compute the cross-covariance sequence associated with the outputs of two rational digital transfer functions driven by the same white noise sequence.
Abstract: A simple linear procedure is given to compute the cross-covariance sequence associated with the outputs of two rational digital transfer functions driven by the same white noise sequence. Such a computation often appears in the study of digital filters, in Wiener filtering, in noise variance estimation, in the study of low-order approximations, and in the study of multichannel systems. A fast algorithm based on the Euclid algorithm is introduced to solve the linear system of equations involved in the computation, and a detailed analysis of the matrix is given. The special case of the autocovariance computation is reviewed, and the same study is performed. Alternate polynomial presentations are given and are shown to involve the same matrices and similar fast algorithms. >
TL;DR: It was shown that information-theoretic criteria for detection of the number of signals under an additive model with white noise when the noise variance is known or unknown are strongly consistent even when the underlying distribution is not necessarily Gaussian.
Abstract: L.C. Zhao et al. (1986) proposed certain information-theoretic criteria for detection of the number of signals under an additive model with white noise when the noise variance is known or unknown. It was shown that these criteria are strongly consistent even when the underlying distribution is not necessarily Gaussian. Upper bounds on the probabilities of error detection are obtained here. >
TL;DR: An effective technique for modeling sparse systems has been developed, and the error surface of the system is analyzed with respect to estimation noise and the techniques for delay determination and corresponding gain adaption are verified.
Abstract: An effective technique for modeling sparse systems has been developed, and the error surface of the system is analyzed with respect to estimation noise. The technique requires a type of adaptive filter which is called an adaptive delay filter. An implementation of the adaptive delay filter is discussed that includes adaptive gains in addition to variable delay taps. The filter is especially applicable to modeling systems with a sparse impulse response. Less computation is required for a sparse system than with the conventional approach. The technique is tested with a variety of unknown systems using both white noise input and autoregressive input. It is shown that it works properly for both sparse and nonsparse systems in noise-free and noisy conditions. The performance of the technique is verified by a careful analysis of the error surface and the techniques for delay determination and corresponding gain adaption. >
TL;DR: In this paper, Zhou and Khargonekar extended Zhou's results to include performance bounds, defined as the worst-case expected value of a quadratic functional involving the state variables when the system is subjected to white noise disturbances.
Abstract: In a recent paper K. Zhou and P.P. Khargonekar (ibid., vol.AC-32, pp.621-623, 1987) obtained sufficient conditions for robust stability over specified sets of matrix perturbations. These results are here extended to include performance bounds. Performance is defined as the worst-case expected value of a quadratic functional involving the state variables when the system is subjected to white noise disturbances. The results are illustrated by considering the gain margin of both an LQG controller and a robustified design obtained by D.S. Bernstein and S.W. Greeley (1986) for J.C. Doyle's examples. >
TL;DR: In this paper, a family of tests for periodic components in a white Gaussian series is proposed, which is based on a statistic which is proportional to the ratio of the maximum periodogram to the trimmed mean of the periodograms.
Abstract: : A family of tests for periodic components in a white Gaussian series is proposed. The test is based on a statistic which is proportional to the ratio of the maximum periodogram to the trimmed mean of the periodograms. The asymptotic distribution of the statistic is obtained. It is shown that the test proposed and Fisher's test have the same asymptotic powers at the alternative hypotheses that the series contains a single periodic component at a non-zero Fourier frequency. The tests are applied to detect the eigenfrequencies of the Earth. The proposed test detects some peaks that Fisher's test fails to detect.
TL;DR: In this article, the eigenstructure of the data covariance matrix is used to obtain high-resolution stacking spectra, where the data are modeled as the superposition of wavefronts.
Abstract: Stacking spectra provide maximum‐likelihood estimates for the stacking velocity, or for the ray parameter, of well separated reflections in additive white noise. However, the resolution of stacking spectra is limited by the aperture of the array and the frequency of the data. Despite these limitations, parametric spectral estimation methods achieve better resolution than does stacking. To improve resolution, the parametric methods introduce a parsimonious model for the spectrum of the data. In particular, when the data are modeled as the superposition of wavefronts, the properties of the eigenstructure of the data covariance matrix can be used to obtain high‐resolution spectra. The traditional stacking spectra can also be expressed as a function of the data covariance matrix and directly compared to the eigenstructure spectra. The superiority of the latter in separating closely interfering reflections is then apparent from a simple geometric interpretation. Eigenstructure methods were originally developed...
TL;DR: In this article, the authors measured the noise power spectra of an inductively coupled plasma mass spectrometer at the same plasma conditions as were those of Sr II emission from the plasma itself.
Abstract: The noise power spectra of {sup 85}Rb{sup +} signal and {sup 93}Nb{sup +} signal from an inductively coupled plasma mass spectrometer were measured at the same plasma conditions as were those of Sr II emission from the plasma itself. Comparison of these spectra showed that discrete frequency noise in the emission at the mass spectrometer sampling orifice is nearly identical with that in the mass spectrometric signal and that white noise in the mass spectrometer signal was higher than that found in the emission signal. The dependence of noise frequencies on plasma operating conditions was generally the same for both measurements and was generally the same as that expected of emission from the plasma alone, i.e., when the plasma was not being sampled for mass spectrometry. However, discrete frequency noise in emission from the plasma alone differed substantially in frequency from that in the mass spectrometric signal. These results indicate that the plasma is the source of discrete frequency noise in the mass spectrometric signal and that the discrete noise frequencies can be affected by changes in plasma gas dynamics due to interaction between the plasma and the mass spectrometer sampling interface. The major source of signal instability in thismore » particular inductively coupled plasma mass spectrometer was found to be 1/f noise.« less
TL;DR: The authors derive a simple, recursive, closed-form algorithm for estimating the parameters of a moving-average (MA) model of known order, using only the autocorrelation and the 1-D diagonal slice of the third-order cumulant of its response to excitation by an unobservable, non-Gaussian, IID process.
Abstract: The authors derive a simple, recursive, closed-form algorithm for estimating the parameters of a moving-average (MA) model of known order, using only the autocorrelation and the 1-D diagonal slice of the third-order cumulant of its response to excitation by an unobservable, non-Gaussian, IID process. The output may be corrupted by zero-mean, nonskewed white noise of unknown variance. The autoregressive moving-average (ARMA) case is briefly discussed. >
TL;DR: It is shown that such minimum norm solution is the maximum-likelihood estimate of the system function parameters and that such an estimate is unbiased, with the lower bound of the variance of the error equal to the Cramer-Rao lower bound, and the upper bound derived from the concept of a generalized inverse.
Abstract: The properties of the maximum likelihood estimator of the generalized p-Gaussian (GPG) probability density function from N independent identically distributed samples is investigated, especially in the context of the deconvolution problem under GPG white noise. Specifically, the properties in the estimator are first described independently of the application. Then the solution of the above-mentioned deconvolution problem is obtained as the solution of a minimum norm problem in an l/sub p/ normed space. It is shown that such minimum norm solution is the maximum-likelihood estimate of the system function parameters and that such an estimate is unbiased, with the lower bound of the variance of the error equal to the Cramer-Rao lower bound, and the upper bound derived from the concept of a generalized inverse. The results are illustrated by computer simulations. >