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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.


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Journal ArticleDOI
TL;DR: A new atom, namely, the dilated and translated windowed exponential frequency modulated functions (FM/sup m/let) is proposed for compactly characterizing both the signal's time-invariant and time-varying spectral contents.
Abstract: We propose a new atom, namely, the dilated and translated windowed exponential frequency modulated functions (FM/sup m/let) for compactly characterizing both the signal's time-invariant and time-varying spectral contents. The superiority of the proposed method to some existing time-frequency distributions (TFDs) is demonstrated using a bat sonar signal.

27 citations

Journal ArticleDOI
TL;DR: A general solution strategy for detecting faulty elements in phased arrays of arbitrary geometries by assuming as input data the amplitude and phase of near-field distributions and allowing to determine the positions of the faulty elements.
Abstract: A general solution strategy for detecting faulty elements in phased arrays of arbitrary geometries is suggested. The proposed deterministic approach assumes as input data the amplitude and phase of near-field distributions and allows to determine the positions of the faulty elements. In particular, the method is founded on the well known Multiple Signal Classification (MUSIC) method, i.e., a spectral estimation technique. The proposed algorithm is also compared with a recently published method by the same authors, against experimental and numerical data. The results fully confirm the usefulness of the proposed technique, highlighting the advantages and the disadvantages of both methods.

27 citations

Proceedings ArticleDOI
18 Sep 2005
TL;DR: In this article, an approach for processing of sonar signals with the ultimate goal of ocean bottom sediment classification is presented, which is based on fractional Fourier transform (FFT) for seafloor sediment classification.
Abstract: In this paper we present an approach for processing of sonar signals with the ultimate goal of ocean bottom sediment classification. Work reported is based on sonar data collected by the volume search sonar (VSS) in the Gulf of Mexico, as well as on VSS synthetic data. The volume search sonar is a beam formed multibeam sonar system with 27 fore and 27 aft beams, covering almost the entire water volume (from above horizontal, through vertical, back to above horizontal). Our investigation is focused on the bottom-return signals since we are interested in determination of the impulse response of the ocean bottom floor. The bottom-return signal is the convolution between the impulse response of the bottom floor and the transmitted sonar chirp signal. The method developed here is based on fractional Fourier transform, a fundamental tool for signal processing and optical information processing. Fractional Fourier transform is a generalization of the classical Fourier transform. The traditional Fourier transform decomposes signal by sinusoids whereas Fractional Fourier transform corresponds to expressing the signal in terms of an orthonormal basis formed by chirps. In recent years, interest in and use of time-frequency tools have increased and become more suitable for sonar applications. The fractional Fourier transform requires finding the optimum order of the transform that can be estimated based on the properties of the chirp signal. The bottom impulse response is given by the magnitude of the fractional Fourier transform applied to the bottom return signal. The technique used in this work has been tested both on synthetic data and real sonar data collected by the VSS. The synthetic sonar return signal has been generated by the convolution between the Green function, which has been utilized to simulate the impulse response of the seafloor and the transmitted VSS chirp. A study is carried out to compare the performance of our method to a conventional method based on deconvolution in the frequency domain (using standard Fourier transform). The amplitude and shape of an acoustic signal reflected from the sea floor is determined mainly by the seabottom roughness, the density difference between water and the sea floor, and reverberation within the substrate. Since the distribution of seafloor types is a very important tool in different applications, a sediment classification has been implemented based on a statistical analysis of the obtained impulse response. In order to perform a robust analysis of the signal, a joint time-frequency analysis is necessary. In this paper the analysis has been evaluated using the Wigner distribution, which can be thought of as a signal energy distribution in joint time-frequency domain. Singular value decomposition of the Wigner distribution has been used in order to perform the seafloor sediment classification. A comparative analysis of the experimental results for classical deconvolution and fractional Fourier method is presented. Results are shown and suggestions for future work are provided

27 citations

Journal ArticleDOI
01 Oct 1998
TL;DR: In this article, the authors describe the application of modern spectral analysis techniques to synthetic aperture radar data, which can improve the geometrical resolution of the image with respect to the numerical values related to the compressed coded waveform and the synthetic aperture, so that subsequent classification procedures will have improved performance.
Abstract: The authors describe the application of modern spectral analysis techniques to synthetic aperture radar data. The purpose is to improve the geometrical resolution of the image with respect to the numerical values related to the compressed coded waveform and the synthetic aperture, so that subsequent classification procedures will have improved performance as well. The classical spectral estimator, i.e. the FFT, produces an image with resolution in azimuth and range bounded by the Rayleigh limits. Super-resolved images are obtained by replacing the FFT with parametric spectral estimators such as those built around an autoregressive model of the dechirped signal. The proposed processing scheme is based on a two-dimensional covariance method. The expected improvement in resolution is discussed together with the results of a simulation analysis. The application of the technique to images captured by an airborne SAR resulted in a resolution gain factor of about two. The paper concludes with a perspective on future research and applications.

27 citations

Proceedings ArticleDOI
A. Vahatalo1, I. Johansson
20 Jun 1999
TL;DR: The VAD for controlling DTX of the GSM AMR (adaptive multi-rate) speech codec is described, which is based on spectral estimation and periodicity detection and incorporates novel methods to estimate background noise and to detect periodic components based on open-loop pitch gain.
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.

27 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202316
202248
202159
2020101
201994
201895