Showing papers on "Spectral density estimation published in 1999"

Statistical and Adaptive Signal Processing: Spectral Estimation, Signal Modeling, Adaptive Filtering and Array Processing

Abstract: Fundamentals of discrete-time signal processing random variables, vectors, and sequences linear signal models nonparametric power spectrum estimation optimum linear filters algorithms and structure for optimum linear filters least-squares filtering and prediction signal modelling and parametric spectral estimation adaptive filters array processing further topics. Appendices: useful results from matrix algebra and optimization theory MATLAB functions.

Applications of the windowed FFT to electric power quality assessment

Abstract: This paper discusses the application of the windowed fast Fourier transform to electric power quality assessment. The windowed FFT is a time windowed version of the discrete time Fourier transform. The window width may be adjusted and shifted to scan through large volumes of power quality data. Narrow window widths are used for detailed analyses, and wide window widths are used to move rapidly across archived power quality data measurements. The mathematics of the method are discussed and applications are illustrated.

Time-frequency MUSIC

Abstract: A new method for the estimation of the signal subspace and noise subspace based on time-frequency signal representations is introduced. The proposed approach consists of the joint block-diagonalization (JBD) of a set of spatial time-frequency distribution matrices. Once the signal and noise subspaces are estimated, any subspace based approach, including the multiple signal classification (MUSIC) algorithm, can be applied for direction of arrival (DOA) estimation. Performance of the proposed time-frequency MUSIC (TF-MUSIC) for an impinging chirp signal using three different kernels is numerically evaluated.

Mammographic structure: data preparation and spatial statistics analysis

Abstract: Detection of tumors in mammograms is limited by the very marked statistical variability of normal structure rather than image noise. This presentation reports investigation of the statistical properties of patient tissue structures in digitized x-ray projection mammograms, using a database of 105 normal pairs of craniocaudal images. The goal is to understand statistical properties of patient structure, and their effects on lesion detection, rather than the statistics of the images per se, so it was necessary to remove effects of the x-ray imaging and film digitizing procedures. Work is based on the log-exposure scale. Several algorithms were developed to estimate the breast image region corresponding to a constant thickness between the mammographic compression plates. Several analysis methods suggest that the tissue within that region, assuming second- order stationarity, is described by a power law spectrum of the form P(f) equals A/f(beta ), where f is radial spatial frequency and (beta) is about 3. There is no evidence of a flattening of the spectrum at low frequencies. Power law processes can have a variety statistical properties that seem surprising to an intuition gained using mildly random processes such as smoothed Gaussian or Poisson noise. Some of these will be mentioned. Since P(f) is approximately a 3rd order pole at zero frequency, spectral estimation is challenging.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Super resolution SAR imaging via parametric spectral estimation methods

Abstract: Super resolution synthetic aperture radar (SAR) image formation via sophisticated parametric spectral estimation algorithms is considered. Parametric spectral estimation methods are devised based on parametric data models and are used to estimate the model parameters. Since SAR images rather than model parameters are often used in SAR applications, we use the parameter estimates obtained with the parametric methods to simulate data matrices of large dimensions and then use the fast Fourier transform (FFT) methods on them to generate SAR images with super resolution. Experimental examples using the MSTAR and Environmental Research Institute of Michigan (ERIM) data illustrate that robust spectral estimation algorithms can generate SAR images of higher resolution than the conventional FFT methods and enhance the dominant target features.

The probability density of spectral estimates based on modified periodogram averages

Abstract: Welch's (1967) method for spectral estimation of averaging modified periodograms has been widely used for decades. Because such an estimate relies on random data, the estimate is also a random variable with some probability density function. Here, the PDF of a power estimate is derived for an estimate based on an arbitrary number of frequency bins, overlapping data segments, amount of overlap, and type of data window, given a correlated Gaussian input sequence. The PDFs of several cases are plotted and found to be distinctly non-Gaussian (the asymptotic result of averaging frequency bins and/or data segments), using the Kullback-Leibler distance as a measure. For limited numbers of frequency bins or data segments, the precise PDF is considerably skewed and will be important in applications such as maximum likelihood tests.

Adaptive filter featuring spectral gain smoothing and variable noise multiplier for noise reduction, and method therefor

Abstract: An adaptive filter is provided featuring a speech spectrum estimator receiving as input an estimated spectral magnitude signal for a time frame of the input signal and generating an estimated speech spectral magnitude signal representing estimated spectral magnitude values for speech in a time frame. A spectral gain generator receives as input the estimated spectral magnitude signal and the estimated speech spectral magnitude signal and generates as output an initial spectral gain signal that yields an estimate of speech spectrum in a time frame of the input signal when the initial spectral gain signal is applied to the spectral signal. A spectral gain modifier receives as input the initial spectral gain signal and generates a modified gain signal by limiting a rate of change of the initial spectral gain signal with respect to the spectral gain over a number of previous time frames. The modified gain signal is then applied to the spectral signal, which is then converted to its time domain equivalent. The value of the noise multiplier is larger when a time frame of the input signal contains more noise than speech and is smaller when a time frame of the input signal contains more speech than noise.

When is instantaneous frequency the average frequency at each time

Abstract: The interpretation of instantaneous frequency has been a subject of interest for many years. One interpretation is that it is the average frequency at each time in the signal. We prove that instantaneous frequency equals the average frequency at each time only when there is symmetry in the instantaneous spectrum, as previous empirical evidence has suggested. Also, when there is such symmetry, the average frequency at each time equals the median frequency at each time.

Two-dimensional matched filtering for motion estimation

Abstract: In this work, we describe a frequency domain technique for the estimation of multiple superimposed motions in an image sequence. The least-squares optimum approach involves the computation of the three-dimensional (3-D) Fourier transform of the sequence, followed by the detection of one or more planes in this domain with high energy concentration. We present a more efficient algorithm, based on the properties of the Radon transform and the two-dimensional (2-D) fast Fourier transform, which can sacrifice little performance for significant computational savings. We accomplish the motion detection and estimation by designing appropriate matched filters. The performance is demonstrated on two image sequences.

Method and apparatus for a tunable high-resolution spectral estimator

Abstract: A tunable high resolution spectral estimator (Fig 18) method and apparatus for encoding and decoding signals, signal analysis and synthesis, and for performing high resolution spectral estimation An encoder coupled with either or both of a signal synthesizer and a spectral analyzer is used to process a frame of a time-based input signal by passing it through a bank of lower order filters and estimating a plurality of lower order covariances from which a plurality of filter parameters may be determined The signal synthesizer includes a decoder for processing the covariances and a parameter transformer for determining filter parameters for an ARMA filter

Engine control signal processing system with frequency analysis by fourier transform algorithm

Abstract: Only predetermined frequency components included in an ion current flowing between an ignition plug and the ground are extracted from the ion current by means of a low pass filter and a high pass filter as a knock detection signal, that is, a signal used for knock detection. This knock detection signal is then subjected to A/D-conversion in an A/D-converter to generate A/D-converted values which are subsequently supplied to a processing unit employed in a digital signal processor. In the processing unit, the A/D-converted values are subjected to a discrete Fourier transform (DFT) frequency analysis. Knock determination based on data obtained from the DFT frequency analysis results in information indicating the operating state of the internal combustion engine. That is, in the frequency analysis of the A/D-converted values obtained as a result of the A/D-conversion of predetermined frequency components of the ion current, a peculiarity such as the occurrence of a knock or the superposition of noise on the signal is clearly recognized.

Limitation of Color Samples for Spectral Estimation from Sensor Responses in Fine Art Painting

Abstract: A new technique is proposed to improve the way of selecting samples used to estimate spectral reflectance from sensor responses in multi-band images. This technique limits the samples of reflectance spectra based on the spectral reflectance estimated by the conventional estimation method, and estimates it again using the limited samples. Vector angle and distance among reflectance spectra as the criteria for the limitation can be applied to improve the estimation of reflectance spectra.

A virtual instrument for time-frequency analysis

Abstract: A virtual instrument for time-frequency analysis is presented. Its realization is based on an order recursive approach to the time-frequency signal analysis. Starting from the short time Fourier transform and using the S-method, a distribution having the auto-terms concentrated as high as in the Wigner distribution, without cross-terms, may be obtained. The same relation is used in a recursive manner to produce higher order time-frequency representations without cross-terms. Thus, the introduction of this new virtual instrument for time-frequency analysis may be of help to the scientists and practitioners in signal analysis. Application of the instrument is demonstrated on several simulated and real data examples.

Estimating the integral non-linearity of A/D-converters via the frequency domain

Abstract: This paper proposes a method to estimate the Integral Non-Linearity of A/D-converters from the lower order output Fourier coefficients of a sinusoidal input. In order to get a high quality estimate, the lower order Fourier coefficients have to be stabilized with respect to the rounding operation of the A/D-converter. For this we use a computationally efficient and easily applied method named wobbling.

Adaptive two-band spectral subtraction with multi-window spectral estimation

Abstract: An improved spectral subtraction algorithm for enhancing speech corrupted by additive wideband noise is described. The artifactual noise introduced by spectral subtraction that is perceived as musical noise is 7 dB less than that introduced by the classical spectral subtraction algorithm of Berouti et al. (1979). Speech is decomposed into voiced and unvoiced sections. Since voiced speech is primarily stochastic at high frequencies, the voiced speech is high-pass filtered to extract its stochastic component. The cut-off frequency is estimated adaptively. Multi-window spectral estimation is used to estimate the spectrum of stochastically voiced and unvoiced speech, thereby reducing the spectral variance. A low-pass filter is used to extract the deterministic component of voiced speech. Its spectrum is estimated with a single window. Spectral subtraction is performed with the classical algorithm using the estimated spectra. Informal listening tests confirm that the new algorithm creates significantly less musical noise than the classical algorithm.

Using a new uncertainty measure to determine optimal bases for signal representations

Abstract: We use a new uncertainty measure, H/sub p/, that predicts the compactness of digital signal representations to determine a good (non-orthogonal) set of basis vectors. The measure uses the entropy of the signal and its Fourier transform in a manner that is similar to the use of the signal and its Fourier transform in the Heisenberg uncertainty principle. The measure explains why the level of discretization of continuous basis signals can be very important to the compactness of representation. Our use of the measure indicates that a mixture of (non-orthogonal) sinusoidal and impulsive or "blocky" basis functions may be best for compactly representing signals.

Voice activity detection for GSM adaptive multi-rate codec

Abstract: This paper describes the VAD (voice activity detection) for controlling DTX (discontinuous transmission) of the GSM AMR (adaptive multi-rate) speech codec. The algorithm is based on spectral estimation and periodicity detection. The VAD contains a 9-band IIR filter bank, which divides input signals into frequency bands. The signal level at each band is calculated. Background noise is estimated in each sub-band. The VAD decision is computed by comparing input signal level and background noise estimate. The algorithm incorporates novel methods to estimate background noise and to detect periodic components based on open-loop pitch gain. A new method is also derived to detect correlated complex signals like music.

Enhancement of SAW laser probe measurements by signal processing

Abstract: Many different types of the SAW laser probe have been used for several years to characterize SAW devices as well as inherent properties of particular materials. Our laser probe is of a modified knife-edge type. Operating with a linear response, it has a high dynamic range. The probe provides full phasor information of the detected signal, and directional properties of the detection process makes it possible to determine the surface tilt components in two spatially orthogonal directions. We first sum up the basic characteristics of the probe. We show several examples where we take advantage of these features combined with signal processing techniques such as the fast Fourier transform. This enables us to concentrate on distinct properties of the devices under test. We thus exploit the spatial frequency domain and, in the basic detection process, the directions of the wavecrests, to enhance measurements of particular properties. Examples include results obtained from measurements on a selection of components containing various structures.

An efficient Fourier transform algorithm for multitone harmonic balance

Abstract: A novel Fourier transform technique is proposed for use in multitone harmonic-balance (HB) simulations. It is shown that computations of multitone distorted spectra reduce to efficient one-dimensional fast Fourier transform operations when certain relationships exist between the sampling rate and frequency components of the signal. The algorithm requires minimal initialization time and is readily incorporated into existing HE tools. It is especially useful when the number of input tones is very large, such as spectral regrowth and noise-power ratio simulations. The method is demonstrated on the example of a 5-GHz MESFET amplifier driven by a quadrature phase shift-keying modulated carrier.

A rank test based approach to order estimation. I. 2-D AR models application

Abstract: In system identification and parametric spectral estimation by two-dimensional (2-D) autoregressive (AR) and 2-D autoregressive moving average (ARMA) models, the order selection problem is often required. In this correspondence, we show that the information of 2-D AR model order is implicitly contained in a correlation matrix. An algorithm for 2-D quarter-plane AR model order determination is proposed. Numerical simulations are presented to show the efficiency of the proposed singular value decomposition (SVD) based algorithm.

Spectrum of Doppler ultrasound signals from nonstationary blood flow

Abstract: A new formulation for the Doppler signal generation process in pulsatile flow has been developed enabling easier identification and quantification of the mechanisms involved in spectral broadening and the development of a simple estimation formula for the measured rms spectral width. The accuracy of the estimation formula was tested by comparing it with the spectral widths found by using conventional spectral estimation on simulated Doppler signals from pulsatile flow. The influence of acceleration, sample volume size, and time window duration on the Doppler spectral width was investigated for flow with blunt and parabolic velocity profiles passing through Gaussian-shaped sample volumes. Our results show that, for short duration windows, the spectral width is dominated by window broadening and that acceleration has a small effect on the spectral width. For long duration windows, the effect of acceleration must be taken into account. The size of the sample volume affects the spectral width of the Doppler signal in two ways: by intrinsic broadening and by the range of velocities passing through it. These effects act in opposite directions. The simple spectral width estimation formula was shown to have excellent agreement with widths calculated using the model and indicates the potential for correcting not only for window and nonstationarity broadening but also for intrinsic broadening.