Revisions to Using Matched Filter To Predict the Existence of An Event

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edited Oct 1, 2015 at 0:50

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I have a stream of signal, and I'm looking for the presence of a certain event (the signal), also I have a "model"(which is a an approximation of the original signal) for that signal, and I will adopt it as my matched filter. So I tried to choose the matched filter, without any mathematical calculation. When I was looking for the signal. I saw a pattern that appears repeatedly. And I expect that pattern is the signal.

Could I cross-correlate the matched filter every unit of time (for example, every 2 seconds) with the signal and see if the value of cross correlation approaches to 1, and based on that decide if that event is existing or not? To make things more clearly, the following is an algorithm of my approach

while (Signal s is Streaming) {
```
 while (Signal s is Streaming)
 {
   S = epoch_the_signal(s)
       x = cross_correlation(S,M)
       if(x > Threshold){
    {
     print "The signal appears"}   
   }
 }
```
What if I choose the matched filter as what is given in the literature (taking the matched filter as to increase the SNR. So I take what I expect to be the signal, and I used the formula, which is shown below to build the matched filter). does that will increase the noise that my model is including it in addition to the signal?

I think the hardest work is how to get the signal, not how to find the matched filter. Especially if it's biomedical signal.

I have a stream of signal, and I'm looking for the presence of a certain event (the signal), also I have a "model"(which is a an approximation of the original signal) for that signal, and I will adopt it as my matched filter. So I tried to choose the matched filter, without any mathematical calculation. When I was looking for the signal. I saw a pattern that appears repeatedly. And I expect that pattern is the signal.

Could I cross-correlate the matched filter every unit of time (for example, every 2 seconds) with the signal and see if the value of cross correlation approaches to 1, and based on that decide if that event is existing or not? To make things more clearly, the following is an algorithm of my approach

while (Signal s is Streaming) {
```
       S = epoch_the_signal(s)
       x = cross_correlation(S,M)
       if(x > Threshold){
         print "The signal appears"}   
    }
```
What if I choose the matched filter as what is given in the literature (taking the matched filter as to increase the SNR. So I take what I expect to be the signal, and I used the formula, which is shown below to build the matched filter). does that will increase the noise that my model is including it in addition to the signal?

I think the hardest work is how to get the signal, not how to find the matched filter. Especially if it's biomedical signal.

I have a stream of signal, and I'm looking for the presence of a certain event (the signal), also I have a "model"(which is a an approximation of the original signal) for that signal, and I will adopt it as my matched filter. So I tried to choose the matched filter, without any mathematical calculation. When I was looking for the signal. I saw a pattern that appears repeatedly. And I expect that pattern is the signal.

Could I cross-correlate the matched filter every unit of time (for example, every 2 seconds) with the signal and see if the value of cross correlation approaches to 1, and based on that decide if that event is existing or not? To make things more clearly, the following is an algorithm of my approach
```
 while (Signal s is Streaming)
 {
   S = epoch_the_signal(s)
   x = cross_correlation(S,M)
   if(x > Threshold)
   {
     print "The signal appears"}   
   }
 }
```
What if I choose the matched filter as what is given in the literature (taking the matched filter as to increase the SNR. So I take what I expect to be the signal, and I used the formula, which is shown below to build the matched filter). does that will increase the noise that my model is including it in addition to the signal?

I think the hardest work is how to get the signal, not how to find the matched filter. Especially if it's biomedical signal.

added 607 characters in body

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edited Sep 30, 2015 at 19:28

hbak

321
1
3
10

I have a stream of signal, and I'm looking for the presence of a certain event (the signal), also I have a "model"(which is a an approximation of the original signal) for that signal, and I will adopt it as my matched filter. So I tried to choose the matched filter, without any mathematical calculation. When I was looking for the signal. I saw a pattern that appears repeatedly. And I expect that pattern is the signal.

Could I cross-correlate the matched filter every unit of time (for example, every 2 seconds) and see if the value of cross correlation approaches to 1, and based on that decide if that event is existing or not?

What if my model, including a noise plus the signal, how much my works will be valid?
Could I cross-correlate the matched filter every unit of time (for example, every 2 seconds) with the signal and see if the value of cross correlation approaches to 1, and based on that decide if that event is existing or not? To make things more clearly, the following is an algorithm of my approach

while (Signal s is Streaming) {
```
       S = epoch_the_signal(s)
       x = cross_correlation(S,M)
       if(x > Threshold){
         print "The signal appears"}   
    }
```
What if I choose the matched filter as what is given in the literature (taking the matched filter as to increase the SNR) does that will increase the noise that my model is including it in addition to the signal?
What if I choose the matched filter as what is given in the literature (taking the matched filter as to increase the SNR. So I take what I expect to be the signal, and I used the formula, which is shown below to build the matched filter). does that will increase the noise that my model is including it in addition to the signal?

I think the hardest work is how to get the signal, not how to find the matched filter. Especially if it's biomedical signal.

I have a stream of signal, and I'm looking for the presence of a certain event (the signal), also I have a "model" for that signal, and I will adopt it as my matched filter.

Could I cross-correlate the matched filter every unit of time (for example, every 2 seconds) and see if the value of cross correlation approaches to 1, and based on that decide if that event is existing or not?

What if my model, including a noise plus the signal, how much my works will be valid?
What if I choose the matched filter as what is given in the literature (taking the matched filter as to increase the SNR) does that will increase the noise that my model is including it in addition to the signal?

I think the hardest work is how to get the signal, not how to find the matched filter. Especially if it's biomedical signal.

I have a stream of signal, and I'm looking for the presence of a certain event (the signal), also I have a "model"(which is a an approximation of the original signal) for that signal, and I will adopt it as my matched filter. So I tried to choose the matched filter, without any mathematical calculation. When I was looking for the signal. I saw a pattern that appears repeatedly. And I expect that pattern is the signal.

Could I cross-correlate the matched filter every unit of time (for example, every 2 seconds) with the signal and see if the value of cross correlation approaches to 1, and based on that decide if that event is existing or not? To make things more clearly, the following is an algorithm of my approach

while (Signal s is Streaming) {
```
       S = epoch_the_signal(s)
       x = cross_correlation(S,M)
       if(x > Threshold){
         print "The signal appears"}   
    }
```
What if I choose the matched filter as what is given in the literature (taking the matched filter as to increase the SNR. So I take what I expect to be the signal, and I used the formula, which is shown below to build the matched filter). does that will increase the noise that my model is including it in addition to the signal?

I think the hardest work is how to get the signal, not how to find the matched filter. Especially if it's biomedical signal.

Un-capitalize, fix a couple of typos.

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edited Sep 30, 2015 at 19:03

MBaz

15.4k
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46

If I have a stream of signal, Andand I'm looking for the presence of a certain event (Thethe signal), Alsoalso I have a "model" for that signal, Andand I will adopt it as my matched filter.

Could I cross-correlate the Matched Filtermatched filter every unit of time (for example, every 2 secondseconds) and see if the value of cross correlation approaches to 1, and based on that I decideddecide if that event is existing or not?
What if my model, including a noise plus the signal, Howhow much my works will be valid?
whatWhat if I choose the matched filter as what is given in the literature (taking the matchmatched filter as to increase the SNR) Doesdoes that will increase the noise that my model is including it in addition to the signal?

I think the hardest work is how to get the signal, not how to find the matched filter. Especially if it's biomedical signal.

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asked Sep 30, 2015 at 18:58

hbak

321
1
3
10

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