Extraction of signals from noise

@inproceedings{Wainstein1970ExtractionOS,
  title={Extraction of signals from noise},
  author={Leonard Wainstein and V. D. Zubakov and Albert A. Mullin},
  year={1970}
}
This book is devoted to problems involving extraction of signals from a background of random noise . By "extraction", we mean not only restoration of unknown (random) signals, but also detection of signals of Add this article to private library Remove from private library Submit corrections to this record View record in the new ADS known form together with measurement of unknown parameters of such signals. Much attention is given to radar problems. Thus, in addition to ordinary radio noise… 
EXTRACTION OF SIGNALS IN THE PRESENCE OF STRONG NOISE : CONCEPTS AND EXAMPLES
  • V.
  • Physics
  • 2007
S/N denotes the signal to noise ratio and Ps, Pn, respectively, are the power of the signal and noise in the bandwidth of the detector. Here, noise is short for all types of broadband disturbances
A general likelihood-ratio formula for random signals in Gaussian noise
  • T. Kailath
  • Computer Science
    IEEE Trans. Inf. Theory
  • 1969
TLDR
It is shown that the likelihood ratio for the detection of a random, not necessarily Gaussian, signal in additive white Gaussian noise has the same form as that for a known signal in white Gaussia noise, suggesting an "estimator-correlator" philosophy for engineering approximation of the optimum receiver.
Optimal Three-Dimensional Matched Filter Processing for Detection of Point-Like Moving Objects in Clutter
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Optimal signal enhancement and detection processing theory is applied to this model and a three-dimensional Fourier matched filter implementation is derived to compute clutter-to-noise ratio (CNR) suppression, signal- to-no noise ratio (SNR) enhancement, and probability of detection and false alarm rate estimates as a function of input single pixel SNR.
Elliptical tiling method to generate a 2-dimensional set of templates for gravitational wave search
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A geometrical method that allows one to cover the corresponding physical parameter space by a set of ellipses, each of them being associated with a given template in the field of gravitational wave data analysis, for the search of damped sine signals.
Optimum estimation of nonstationary Gaussian signals in noise
  • T. Kadota
  • Mathematics, Computer Science
    IEEE Trans. Inf. Theory
  • 1969
TLDR
The maximum likelihood and the minimum mean-square estimates of s(t) for each t are presented, by observing the entire signal-plus-noise waveform x(\cdot) during the interval [T_{1], T_{2}] and it is explicilyty proved that both estimates are given by ŝ(t), which is a generalization of the classical Wiener filtering theory for stationary processes.
Fast radio burst detection in the presence of coloured noise
In this paper, we investigate the impact of correlated noise on fast radio burst (FRB) searching. We found that 1) the correlated noise significantly increases the false alarm probability; 2) the
Data analysis strategies for the detection of gravitational waves in non-Gaussian noise
TLDR
A simple two-component noise model that consists of a background of Gaussian noise as well as stochastic noise bursts is presented that shows that the optimal statistic for the non-Gaussian noise model can be approximated by a simple coincidence detection strategy.
Random template placement and prior information
TLDR
Defining specific figures of merit allows one to combine both template metric and prior distribution and devise optimal sampling schemes over the parameter space and an example related to the gravitational wave signal from a binary inspiral event is shown.
Random template placement and prior information
TLDR
Defining specific figures of merit allows one to combine both template metric and prior distribution and devise optimal sampling schemes over the parameter space and an example related to the gravitational wave signal from a binary inspiral event is shown.
Random template placement and prior information
TLDR
Defining specific figures of merit allows one to combine both template metric and prior distribution and devise optimal sampling schemes over the parameter space and an example related to the gravitational wave signal from a binary inspiral event is shown.
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