Spectral density estimation

About: Spectral density estimation is a research topic. Over the lifetime, 5391 publications have been published within this topic receiving 123105 citations.

Generalized Expectation Consistent Signal Recovery for Nonlinear Measurements

Abstract: In this paper, we propose a generalized expectation consistent signal recovery algorithm to estimate the signal $\mathbf{x}$ from the nonlinear measurements of a linear transform output $\mathbf{z}=\mathbf{A}\mathbf{x}$. This estimation problem has been encountered in many applications, such as communications with front-end impairments, compressed sensing, and phase retrieval. The proposed algorithm extends the prior art called generalized turbo signal recovery from a partial discrete Fourier transform matrix $\mathbf{A}$ to a class of general matrices. Numerical results show the excellent agreement of the proposed algorithm with the theoretical Bayesian-optimal estimator derived using the replica method.

High-resolution two-dimensional spectral analysis

Abstract: Until recently most high-resolution autoregressive spectral analysis techniques have been applied to analysis of either single-channel waveforms or multichannel vector processes. However, the use of data prediction autoregressive spectral analysis permits the application of some high-resolution single-channel methods to high-resolution spectral analysis of data fields in two or more dimensions. The technique consists of extrapolating observed data beyond the observation window by means of an autoregressive data-generation model. High-resolution spectral analyses are then obtained by conventional discrete Fourier transforms (DFTs) of the extrapolated data.

Comparison of the performance of three maximum doppler frequency estimators coupled with different spectral estimation methods

Abstract: The performance of three spectral techniques (FFT, AR Burg and ARMA) for maximum frequency estimation of the Doppler spectra is described. Different definitions of fmax were used: frequency at which spectral power decreases down to 0.1 of its maximum value, modified threshold crossing method (MTCM) and novel geometrical method. "Goodness" and efficiency of estimators were determined by calculating the bias and the standard deviation of the estimated maximum frequency of the simulated Doppler spectra with known statistics. The power of analysed signals was assumed to have the exponential distribution function. The SNR ratios were changed over the range from 0 to 20 dB. Different spectrum envelopes were generated. A Gaussian envelope approximated narrow band spectral processes (P. W. Doppler) and rectangular spectra were used to simulate a parabolic flow insonified with C. W. Doppler. The simulated signals were generated out of 3072-point records with sampling frequency of 20 kHz. The AR and ARMA models order selections were done independently according to Akaike Information Criterion (AIC) and Singular Value Decomposition (SVD). It was found that the ARMA model, computed according to SVD criterion, had the best overall performance and produced results with the smallest bias and standard deviation. In general AR(SVD) was better than AR(AIC). The geometrical method of fmax estimation was found to be more accurate than other tested methods, especially for narrow band signals.

Bispectrum of radar signatures and application to target classification

Abstract: The scattering characteristics of a radar target can be specified by its impulse response. The impulse response can be computed using Fourier spectral estimation techniques on samples of the radar scattering obtained at a number of equally spaced frequencies. Usually, there is a clear relationship between the location of specific scattering mechanisms and the time such mechanisms appear in the impulse response. One of the difficulties in this type of analysis, however, is that complex targets often have multiple scattering interactions. Many of the terms in the impulse response are due to these interactions rather than specular subcomponent scattering. The bispectrum displays the specifics of the interactions of both single- and multiple-reflection mechanisms. In addition, the suppression of Gaussian noise makes the bispectrum a source of robust features for classification of radar targets. Identification of multiple interaction terms as well as noise suppression are demonstrated in this paper. Finally, classification of scale models of commercial aircraft based on their bispectral signatures is performed.

Means and method for measuring power system frequency

Abstract: Samples from a first and second cycle of a system signal are used to generate discrete Fourier transforms for the respective first and second cycle. Multiplication of the discrete Fourier transform of the second cycle by the complex conjugate of the discrete Fourier transform of the first cycle produces a frequency deviation phasor whose angle with respect to the real axis is representative of the frequency deviation of the system signal from a predetermined reference frequency. Actual system frequency may be determined by obtaining the inverse tangent of the angle of the frequency deviation phasor with respect to the real axis. Apparatus for determining the frequency and frequency deviation of a system signal is disclosed as is a method and apparatus for determining frequency and frequency deviation of a system signal comprising a plurality of phases.