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''in a noise background''
 
''in a noise background''
  
 
A branch of statistical communication theory. The mathematical problems of signal extraction are statistical problems of the theory of stochastic processes (see also [[Information theory|Information theory]]). A number of typical problems in signal extraction are listed below.
 
A branch of statistical communication theory. The mathematical problems of signal extraction are statistical problems of the theory of stochastic processes (see also [[Information theory|Information theory]]). A number of typical problems in signal extraction are listed below.
  
A message <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s085/s085050/s0850501.png" />, which may be a random or a non-random function of a certain structure, is converted into a signal <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s085/s085050/s0850502.png" />, where <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s085/s085050/s0850503.png" /> is a stochastic process (a noise), while <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s085/s085050/s0850504.png" /> (the communication channel) is an operator which converts the pair <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s085/s085050/s0850505.png" /> into the received signal <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s085/s085050/s0850506.png" />. It is usually assumed that the effect of the noise on the signal is additive: <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s085/s085050/s0850507.png" />. In such a situation the problems of signal extraction are as outlined below.
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A message $s(t)$, which may be a random or a non-random function of a certain structure, is converted into a signal $x(t)=V(s,n)$, where $n(t)$ is a stochastic process (a noise), while $V$ (the communication channel) is an operator which converts the pair $(s,n)$ into the received signal $x$. It is usually assumed that the effect of the noise on the signal is additive: $x(t)=s(t)+n(t)$. In such a situation the problems of signal extraction are as outlined below.
  
1) Detection of the signal, that is, checking the hypothesis <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s085/s085050/s0850508.png" /> (a signal is present) against the alternative <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s085/s085050/s0850509.png" /> (a signal is absent). More involved varieties of the initial hypothesis are also studied: <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s085/s085050/s08505010.png" /> starting from some moment, possibly random, of time <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s085/s085050/s08505011.png" />, which is the moment of appearance of the signal. Here arises the problem of estimating <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s085/s085050/s08505012.png" />.
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1) Detection of the signal, that is, checking the hypothesis $x(t)=s(t)+n(t)$ (a signal is present) against the alternative $x(t)=n(t)$ (a signal is absent). More involved varieties of the initial hypothesis are also studied: $x(t)=s(t)+n(t)$ starting from some moment, possibly random, of time $\tau$, which is the moment of appearance of the signal. Here arises the problem of estimating $\tau$.
  
2) Differentiating between signals, that is, checking the hypothesis <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s085/s085050/s08505013.png" />, <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s085/s085050/s08505014.png" />, against the hypothesis <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s085/s085050/s08505015.png" />, <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s085/s085050/s08505016.png" />, where <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s085/s085050/s08505017.png" /> and <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s085/s085050/s08505018.png" /> are two different sets of signals.
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2) Differentiating between signals, that is, checking the hypothesis $x(t)=s(t)+n(t)$, $s\in S_1$, against the hypothesis $x(t)=s(t)+n(t)$, $s\in S_2$, where $S_1$ and $S_2$ are two different sets of signals.
  
3) Filtering (reconstruction of a signal), that is, finding statistical estimates for the values of the signal <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s085/s085050/s08505019.png" /> at a point <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s085/s085050/s08505020.png" />, after <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s085/s085050/s08505021.png" />, <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s085/s085050/s08505022.png" />, has been received.
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3) Filtering (reconstruction of a signal), that is, finding statistical estimates for the values of the signal $s(t)$ at a point $t$, after $x(t)$, $t\in T$, has been received.
  
 
See also [[Statistical hypothesis|Statistical hypothesis]]; [[Stochastic processes, filtering of|Stochastic processes, filtering of]].
 
See also [[Statistical hypothesis|Statistical hypothesis]]; [[Stochastic processes, filtering of|Stochastic processes, filtering of]].

Latest revision as of 14:54, 13 August 2014

in a noise background

A branch of statistical communication theory. The mathematical problems of signal extraction are statistical problems of the theory of stochastic processes (see also Information theory). A number of typical problems in signal extraction are listed below.

A message $s(t)$, which may be a random or a non-random function of a certain structure, is converted into a signal $x(t)=V(s,n)$, where $n(t)$ is a stochastic process (a noise), while $V$ (the communication channel) is an operator which converts the pair $(s,n)$ into the received signal $x$. It is usually assumed that the effect of the noise on the signal is additive: $x(t)=s(t)+n(t)$. In such a situation the problems of signal extraction are as outlined below.

1) Detection of the signal, that is, checking the hypothesis $x(t)=s(t)+n(t)$ (a signal is present) against the alternative $x(t)=n(t)$ (a signal is absent). More involved varieties of the initial hypothesis are also studied: $x(t)=s(t)+n(t)$ starting from some moment, possibly random, of time $\tau$, which is the moment of appearance of the signal. Here arises the problem of estimating $\tau$.

2) Differentiating between signals, that is, checking the hypothesis $x(t)=s(t)+n(t)$, $s\in S_1$, against the hypothesis $x(t)=s(t)+n(t)$, $s\in S_2$, where $S_1$ and $S_2$ are two different sets of signals.

3) Filtering (reconstruction of a signal), that is, finding statistical estimates for the values of the signal $s(t)$ at a point $t$, after $x(t)$, $t\in T$, has been received.

See also Statistical hypothesis; Stochastic processes, filtering of.

References

[1] W.B. Davenport, W.L. Root, "An introduction to the theory of random signals and noise" , McGraw-Hill (1970)
[2] A.A. Kharkevich, "Channels with noise" , Moscow (1965) (In Russian)


Comments

References

[a1] J.M. Wozencraft, I.M. Jacobs, "Principles of communication engineering" , Wiley (1965)
[a2] C.W. Helstrom, "Statistical theory of signal detection" , Pergamon (1968)
How to Cite This Entry:
Signal extraction. Encyclopedia of Mathematics. URL: http://encyclopediaofmath.org/index.php?title=Signal_extraction&oldid=32901
This article was adapted from an original article by I.A. Ibragimov (originator), which appeared in Encyclopedia of Mathematics - ISBN 1402006098. See original article