Spectrum Estimation Techniques Research Articles

A comparison of spectrum estimation techniques for sensorless speed detection in induction machines

This paper compares digital spectrum estimation techniques which can be used to extract speed information from rotor slot and eccentricity harmonics contained in the stator current. In previous work, speed-related current harmonics have been shown to improve the performance of existing back-EMF-based sensorless schemes, since these harmonics are parameter independent and exist at virtually any nonzero speed. Digital filtering, however, requires a minimum data sampling time in order to achieve the desired resolution. The contribution of this paper is to determine the optimal method for accurately extracting the speed-related harmonics in the least amount of time. Several digital signal processing algorithms are investigated, including the fast Fourier transform and other traditional methods, as well as parametric techniques which can provide improved spectrum estimation for short data records. Each approach is evaluated on the criteria of accuracy, robustness, and computation time given a short data record.

A double-talk detector based on coherence

We address the problem of detecting double-talk in a full duplex transmission line. A new double-talk detector (DTD) based on measuring the similarity between the far- and near-end speech signals is proposed. The detector is block oriented and operates in the frequency domain where the similarity between the signals is measured by means of the coherence function. The coherence is estimated with a short sequence of data by exploiting the multiple window spectrum estimation technique. Theoretical evaluation and examples of its performance are presented. The proposed DTD operates accurately in a wide range of situations, i.e., a difference in speech levels and hybrid attenuations ranging from 0 to 20 dB.

Multi‐window spectrum estimation — A linear algebraic approach

AbstractThe purpose of this paper is to elucidate the multi‐window method for spectral estimation and sinusoid detection. The multi‐window method, devised by D. J. Thomson, has many features that distinguish it from other spectrum estimation techniques. It has an elegant derivation, starting from first principles and using optimality arguments. It lends itself to performance analysis and provides a built‐in measure of its reliability. In contrast to Thomson's integral equation derivation, we use linear algebraic methods both to treat the problem and to develop the solution. In addition to the tutorial, we present an overview of published generalizations and analyses of the method.

Fast finite-difference time-domain analysis of resonators using digital filtering and spectrum estimation techniques

The use of digital filtering and spectrum estimation techniques for improving the efficiency of the FD-TD algorithm in solving eigenvalue problems is discussed. The great improvement of the efficiency of the method is demonstrated by means of both numerical and measurement results. In addition, several improvements to the present FD-TD method for eigenvalue analysis are presented. These include the analysis of open dielectric resonators and the extraction of the resonant frequencies from the FD-TD results. The result for the open dielectric resonator analysis is validated using measured data. >

Simulating and processing the Arctic acoustics due to ice dynamics

Under sea-ice noise, in particular that produced by ice floes in the Arctic region, is studied by means of a model simulation and the development of signal processing techniques for its measurement and estimation. This noise is due to the interaction among ice floes that produces non-Gaussian noise in time and frequency. Based on previous studies, a Gaussian shape acoustic wave pressure generated by a single collision is assumed. The random occurrence of the ice collision process is modeled as a Poisson sequence of impulses. Different sonar array spectrum estimation techniques [Fourier, autoregressive (AR), and minimum variance (MV)] are analyzed for the processing of the resulting acoustic signal. It is found that the minimum variance spectral estimation technique is the overall best for this type of signal for a wide range of the model parameters. Previously reported studies have often used the periodogram technique that as shown here usually gives poor results.

COMMUNICATION SYSTEM IDENTIFICATION THROUGH ADAPTIVE MODELING OF THE COMMUNICATION CHANNEL

In this paper,attempts are made to profile the radio propagation path linking an intercepted transmitter site to the signal collection facility. The main objective is to extract fingerprinting clues to identify the transmitter locality. Different methods of power spectrum estimation techniques are applied and compared w.r.t. their usefulness in identifying the multipath channel. A novel system is proposed to act as a transmitter locality identifier. The system is based on adaptive modeling of the faded channel. The system configuration is presented.,the control software is developed and sample run-outs of the developed programs are presented and commented.

Roundoff noise analysis for digital signal power processors using Welch's power spectrum estimation

In this paper, we present an analysis for the noise due to finite word length effects for digital signal power processors using Welch's power spectrum estimation technique to measure the power of Gaussian random signals over a frequency band of interest. The input of the digital signal processor contains a finite-length time interval in which the true Gaussian signal is contaminated by Gaussian noise. We analytically derive the roundoff noise-to-signal ratio in the measurement of the signal power. We also present computer simulations which validate the analytical results. These results can be used in tradeoff studies of hardware design such as number of bits required at each processing stage. The results presented in this paper are currently being used in the design of a digital Doppler processor [1], [2] for a radar scatterometer [3]-[5].

Recent trends in random data analysis.

Recent trends in random data analysis with parametric and nonparametric approaches are reviewed. First, for nonparametric spectrum analysis, the effectiveness of using data windows is emphasized with an example. Secondly, based on a recursive algorithm by Levinson and Durbin, various autoregressive (AR) spectrum estimation techniques by Yule-Walker, Burg and others are explained together with their specific properties. Also, selection criteria for the order of AR model fitting are briefly discussed. As for autoregressive moving average (ARMA) spectrum estimation, the high order Yule-Walker method and its variants are mentioned. Finally, a periodic AR model is introduced for treating random data with periodic structures. An example of applying this model to real temperature data is also given.

Epicardial electrical activation analyzed via frequency-wavenumber spectrum estimation for the characterization of arrhythmiagenic states

The application of a new signal processing methodology to the analysis of epicardial array ECG signals is presented as an alternative to isopotential or isochrones mapping by the use of a zero-delay wavenumber spectrum (ZDWS) estimation technique. The methodology "explains" the array data as the sum of modulated wideband (non-sinusoidal) propagating waves projected onto the array plane and provides an accurate estimate of their number and bearing. The slowness distribution of each of the waves is then obtained by estimating their temporal spectrum. In this experimental study the effects of localized noninfarcting reversible low flow ischemia, digoxin toxicity and verapamil reversal of digoxin toxicity are quantified via the ZDWS methodology and are compared with the information that can be extracted from isopotential mapping. It is demonstrated that the ZDWS methodology permits the epicardial electric activation to be decomposed into a number of quantification parameters which possess a hierarchical "tree" structure and therefore provide a means for an objective and robust characterization of the effects of agents which alter myocardial conduction and arrhythmia generation.

Comments, with reply, on "ARMA spectral estimation of time series with missing observations" by B. Porat and B. Friedlander

A variant to a recently proposed autoregressive moving average (ARMA) spectrum estimation technique for time series with gapped data is suggested. It is based on the partial fraction expansion of the power spectrum and exhibits some computational and operational advantages.

Fourier analysis on ungated time-activity curves.

The frequency content of the ungated radionuclide time-activity curves for left and right ventricles was analyzed using a power spectrum estimation technique. This technique was applied to ten patients. It was observed that 75% of the contraction power of any ventricle was contained in the fundamental and at least 98% contained in the fundamental plus the first harmonic. Patients presenting bigeminy and trigeminy had a completely different spectrum distribution which calls for a different interpretation. Low frequency spectrum lines have been observed at breathing frequency. Up to 62% of the total power may be contained in those spectrum lines.

A new approach to array geometry for improved spatial spectrum estimation

In this letter, we address the problem of element placement in a linear aperiodic array for use in spatial spectrum estimation. By making use of a theorem by Caratheodory, it is shown that, for a given number of elements, there exists a distribution of element positions which, for uncorrelated sources, results in superior spatial spectrum estimators than are otherwise achievable. The improvement is obtained by constructing an augmented covariance matrix, made possible by the choice of element positions, with dimension greater than the number of array elements. The augmented matrix is then used in any of the known spectrum estimation methods in conjunction with a correspondingly augmented search pointing vector. Examples are given to show the superior detection capability, the larger dynamic range for spectral peak to background level, the lower sidelobes, and the relatively low bias values, when one of the known spectrum estimation techniques based on eigenstructure is used.

A general lower bound for parametric spectrum estimation

The paper presents a lower bound on the variance of parametric spectral estimators. The bound is potentially useful for evaluating the performance of existing and newly proposed spectrum estimation techniques. Explicit formulas are given for ARMA and AR-plus-noise models. The behavior of the bound is illustrated by several examples.

Iterative nonparametric spectrum estimation

Starting with the traditional spectrum estimation technique, a new iterative nonparametric algorithm is developed. In each iteration of the algorithm, the effect of the previous spectral estimate is removed from the original signal by inverse filtering by a minimum-phase impulse response to obtain a residual signal whose spectral characteristics are then estimated to improve the previous estimate. The algorithm in each iteration decreases the bias of the resulting spectral estimate, without increasing its final variance. Identification of the corresponding system minimum-phase finite duration impulse response is inherent in the proposed procedure. The whole procedure can be carried out with the numerical stability of FFT's, without solving any kind of equations. This algorithm is not suitable for very narrowband spectral shapes estimation from small amounts of data. Properties of the proposed estimator are derived in detail. Experimental results show that the algorithm converges in a small number of iterations, and demonstrate that the algorithm provides very accurate spectral estimates compared with parametric methods based on ARMA models.

High-resolution beamforming with oversampled arrays

Interpreting plane-wave beamforming as wave-vector spectrum estimation, we show that the maximum entropy spectrum estimation technique (MEM) indicates the presence of false targets when used to beamform a spatially oversampled array. The source of this instability lies in the presence in the MEM technique of the prior estimate that the wave-vector spectrum is white over the Nyquist band. By the use of a new, adaptive, high-resolution spectral estimator, which includes MEM as a special case, the instability is removed. The results are of particular significance for short arrays operated at low frequencies.

Equal observation interval comparison of maximum entropy and weighted overlapped segment averaging spectrum estimation techniques

This correspondence presents a brief summary of an experimental comparison between conventional spectral estimation methods and a maximum entropy spectrum analysis (MESA) algorithm. These results should be of some interest to workers in acoustical signal processing, especially sonar and surveillance, due to several factors in the form of the experimentation. Specifically, these factors are the range of signal-to-noise ratio (SNR) studied, comparisons based on equal length observation intervals, the use of weighted overlapped segment averaging (WOSA), and the use of ensemble averaging after maximum entropy analysis. Results are presented for resolution and peak signal response and tend to indicate that the MESA method shows promise of achieving the detection performance of long observation interval discrete Fourier transform analysis at a reduced observation time for a useful SNR range.

Aperture Sampling Techniques for Precision Direction Finding

The resolution and tracking of radar targets which are close to. gether is a classical problem which is receiving increased attention. Improving the angular resolution by sampling the received wave front along the aperture and then applying modern "high resolution" spectrum estimation techniques (e. g., maximum likelihood and maximum entropy processing) to the spatial sample data is discussed. Experimental results are presented for the application ion of these techniques to field data from a L- band line array.

Spectrum Estimation Techniques for Characterization and Development of WT4 Waveguide-I

Techniques for reliably estimating the power spectral density function for both small and large samples of a stationary stochastic process are described. These techniques have been particularly successful in cases where the range of the spectrum is large. The methods are resistant to a moderate amount of contaminated or erroneous data and are well suited for use with auxiliary tests for stationarity and normality. Part I is concerned with background and theoretical considerations while examples from the development and analysis of the WT4 waveguide medium will be discussed in Part II, next issue.

Power-spectrum identification in terms of rational models

A technique is described for the identification of unknown power-spectral densities from sampled data in terms of a rational function of z . The problem is reduced to the minimization of a function of K parameters, where K is the order of the numerator of the model. This criterion, called the minimum residual criterion, reduces to the maximum likelihood criterion when the observed signal is Gaussian. A computational technique is described for minimizing this function which uses filtering and correlation to obtain the gradient and an iterative descent method due to M. J. D. Powell for minimization. Some computational results are given in which the method is compared with all-pole and conventional spectrum estimation techniques.