Topic
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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13 Oct 2003
TL;DR: Using some useful properties of the amplitude spectrum of MFSK signals, a fast Fourier transform based classifier (FFTC) of M FSK signals has been developed and it is found that the FFTC algorithm works well in classifying 2-FSK, 4- FSK, 8-FS K, 16-FS k, and 32-FSk signals when SNR>0dB.
Abstract: The existing decision-theory based classifiers for M-ary frequency shift keying (MFSK) signals have assumed that there is some prior knowledge of the transmitted MFSK signal parameters; while the feature-based classifiers have some limitations such as that their thresholds are signal-to-noise-ratio-dependent (SNR-dependent). In this paper, we investigate some useful properties of the amplitude spectrum of MFSK signals. Using these properties as classification criteria, a fast Fourier transform based classifier (FFTC) of MFSK signals has been developed. The FFTC algorithm is practical since it only requires some reasonable knowledge of a received signal. It is found that the FFTC algorithm works well in classifying 2-FSK, 4-FSK, 8-FSK, 16-FSK, and 32-FSK signals when SNR>0dB. The FFTC algorithm also gives good estimation of the frequency deviation of the received MFSK signal.
33 citations
TL;DR: In this article, the authors proposed a zero-lag inverse filter for the detection of refracted arrivals from explosions up to 1350 km away and for estimation of spectra of microseismic noise observed at the time of each shot.
Abstract: Least‐squares, zero‐lag inverse filters may be used for predictive deconvolution of stationary time series and for obtaining autoregressive or maximum entropy spectral estimates. The greatest problem in finding such an inverse filter is determining the optimum operator length for a given finite length of data. The identical problem of determining the correct order of an autoregressive model for the data has been solved by Akaike, whose final prediction error (FPE) statistic is a minimum for the optimum length model. This minimum FPE criterion may be applied to both single and multiple time series. The FPE procedure has been used successfully on simultaneous three‐component seismometer and hydrophone data for the detection of refracted arrivals from explosions up to 1350 km away and for estimation of spectra of microseismic noise observed at the time of each shot. The data were recorded with an ocean bottom seismometer.
33 citations
07 Sep 2003
TL;DR: This work addresses channel estimation based on the discrete Fourier transform (DFT) applied to OFDM-based MIMO systems and shows the proposed estimator to be optimum for the sample spaced channel, i.e. the channel tap delays are multiples of the sampling duration.
Abstract: We addresses channel estimation based on the discrete Fourier transform (DFT) applied to OFDM-based MIMO systems. By exploiting the properties of the DFT, channel estimation schemes for MIMO-OFDM system can be simplified. The Fourier transform translates phase shifts in the frequency domain to delays in the time domain. In order to exploit this feature, phase shifted pilot sequences are a perfect match to the Fourier transform in terms of separating the N/sub T/ superimposed signals, corresponding to N/sub T/ transmit antennas, without any further processing. The proposed estimator is shown to be optimum for the sample spaced channel, i.e. the channel tap delays are multiples of the sampling duration. A sub-optimum approximation for the non-sample spaced channel is also suggested.
33 citations
TL;DR: In this article, a numerical procedure to estimate the wave spectrum based on measured motions of vessels at zero advance speed is presented, where a parametric form is fitted by using minimization of squared errors between nonparametric and parametric forms.
33 citations
TL;DR: In this article, the authors proposed a spectral estimator based on a modification of the standard slotting technique, known as "local scaling" or "local normalization", in conjunction with a window of variable width.
Abstract: The spectral density function of turbulent velocity fluctuations can be estimated from randomly sampled LDA data by first computing a discretized autocorrelation function (using the slotting technique) followed by a Fourier transform of this function. The spectral estimates obtained in this way have a large statistical scatter in the high-frequency range. This work focuses on a spectral estimator with a much reduced statistical scatter (approximately 1 decade), enabling the retrieval of the spectral density function up to higher frequencies. This spectral estimator is based on a modification of the standard slotting technique, known as ‘local scaling’ or ‘local normalization’, in conjunction with a window of variable width. A series of benchmark tests for spectral estimators indicated that this estimator yields good overall results. This paper explores the characteristics of the new spectral estimator with regard to the effects of velocity bias and the presence of uncorrelated noise in the velocity data.
33 citations