How does one measure the Fourier components of a signal?What kind of hardware implements Fourier transform?Time-limited signal Fourier transformHow does Fourier series apply to signals?Fourier Series Expansion of Periodic Signalfourier transform for non-stationary signalFourier transform and signal envelopeHow to apply the Fourier Transform to this?Fourier Transform and the Delta FunctionMagnitude of fourier transform of AM signalFourier coefficients of a double frequency signalFourier transform of a signal

Opposite of a diet

Have I saved too much for retirement so far?

How do I rename a LINUX host without needing to reboot for the rename to take effect?

How was Earth single-handedly capable of creating 3 of the 4 gods of chaos?

Using parameter substitution on a Bash array

Bash method for viewing beginning and end of file

Why is `const int& k = i; ++i; ` possible?

Dot above capital letter not centred

Lay out the Carpet

voltage of sounds of mp3files

Ways to speed up user implemented RK4

Personal Teleportation as a Weapon

There is only s̶i̶x̶t̶y one place he can be

Applicability of Single Responsibility Principle

Is exact Kanji stroke length important?

What is difference between behavior and behaviour

What't the meaning of this extra silence?

Understanding "audieritis" in Psalm 94

Is there any reason not to eat food that's been dropped on the surface of the moon?

Can I use my Chinese passport to enter China after I acquired another citizenship?

Implement the Thanos sorting algorithm

If you attempt to grapple an opponent that you are hidden from, do they roll at disadvantage?

Stereotypical names

Curses work by shouting - How to avoid collateral damage?



How does one measure the Fourier components of a signal?


What kind of hardware implements Fourier transform?Time-limited signal Fourier transformHow does Fourier series apply to signals?Fourier Series Expansion of Periodic Signalfourier transform for non-stationary signalFourier transform and signal envelopeHow to apply the Fourier Transform to this?Fourier Transform and the Delta FunctionMagnitude of fourier transform of AM signalFourier coefficients of a double frequency signalFourier transform of a signal













5












$begingroup$


I attended a lecture on ground penetrating radar (GPR). I am used to reflection seismic where the incoming pressure amplitude is measured as a function of time.

In GPR, instead the Fourier components are measured. That is the amplitude and phase as a function of frequency. Then these data are Fourier transformed to find the amplitude as a function of time.



I am curious about how the electronics recording in Fourier space works.
Is the signal split by many narrow band pass filters in parallel?
Are these filters RLC circuits?
Are there chips that contains many such filters in parallel?










share|improve this question











$endgroup$







  • 2




    $begingroup$
    Parallel data handling is not a requirement if a moving window is used over the samples. Although it's quite possible with FPGAs, it's often not practical/economical to handle the data without serialising it first.
    $endgroup$
    – Mast
    Mar 17 at 17:09















5












$begingroup$


I attended a lecture on ground penetrating radar (GPR). I am used to reflection seismic where the incoming pressure amplitude is measured as a function of time.

In GPR, instead the Fourier components are measured. That is the amplitude and phase as a function of frequency. Then these data are Fourier transformed to find the amplitude as a function of time.



I am curious about how the electronics recording in Fourier space works.
Is the signal split by many narrow band pass filters in parallel?
Are these filters RLC circuits?
Are there chips that contains many such filters in parallel?










share|improve this question











$endgroup$







  • 2




    $begingroup$
    Parallel data handling is not a requirement if a moving window is used over the samples. Although it's quite possible with FPGAs, it's often not practical/economical to handle the data without serialising it first.
    $endgroup$
    – Mast
    Mar 17 at 17:09













5












5








5


1



$begingroup$


I attended a lecture on ground penetrating radar (GPR). I am used to reflection seismic where the incoming pressure amplitude is measured as a function of time.

In GPR, instead the Fourier components are measured. That is the amplitude and phase as a function of frequency. Then these data are Fourier transformed to find the amplitude as a function of time.



I am curious about how the electronics recording in Fourier space works.
Is the signal split by many narrow band pass filters in parallel?
Are these filters RLC circuits?
Are there chips that contains many such filters in parallel?










share|improve this question











$endgroup$




I attended a lecture on ground penetrating radar (GPR). I am used to reflection seismic where the incoming pressure amplitude is measured as a function of time.

In GPR, instead the Fourier components are measured. That is the amplitude and phase as a function of frequency. Then these data are Fourier transformed to find the amplitude as a function of time.



I am curious about how the electronics recording in Fourier space works.
Is the signal split by many narrow band pass filters in parallel?
Are these filters RLC circuits?
Are there chips that contains many such filters in parallel?







high-frequency fourier radar






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Mar 18 at 3:56









Mast

5421418




5421418










asked Mar 17 at 9:47









AndyAndy

1565




1565







  • 2




    $begingroup$
    Parallel data handling is not a requirement if a moving window is used over the samples. Although it's quite possible with FPGAs, it's often not practical/economical to handle the data without serialising it first.
    $endgroup$
    – Mast
    Mar 17 at 17:09












  • 2




    $begingroup$
    Parallel data handling is not a requirement if a moving window is used over the samples. Although it's quite possible with FPGAs, it's often not practical/economical to handle the data without serialising it first.
    $endgroup$
    – Mast
    Mar 17 at 17:09







2




2




$begingroup$
Parallel data handling is not a requirement if a moving window is used over the samples. Although it's quite possible with FPGAs, it's often not practical/economical to handle the data without serialising it first.
$endgroup$
– Mast
Mar 17 at 17:09




$begingroup$
Parallel data handling is not a requirement if a moving window is used over the samples. Although it's quite possible with FPGAs, it's often not practical/economical to handle the data without serialising it first.
$endgroup$
– Mast
Mar 17 at 17:09










2 Answers
2






active

oldest

votes


















7












$begingroup$


Is the signal split by many narrow band pass filters in paralell? Are these filters RLC circuits? Are there chips that contains many such filters in paralell?




No, these are typically collected as time signal, and then transformed digitally (by a DFT implemented by an FFT) to discrete Fourier domain for processing reasons.



In the sense that yes, there's many hardware implementations of FFTs, and the FFT can be considered as polyphase boxcar filterbank, there's chips containing a filterbanks – but they're processing a digitized time signal, not an analogue one.



(The reasons being pulse compression, mainly, i.e. you need to do a huge correlation with the known signal to even see your signal from the noise – there isn't exactly much signal coming back to the satellite after being radiated from lower earth orbit to ground, through the ground, scattered there, and back. Correllation is very computationally intense, as it goes with the square of the length of the signal, but with processing in frequency domain, you can significantly reduce that amount, and bonus, the data might become way easier to compress, store and transmit to the ground station.)



(By the way, I wrote a top-of-my-head list of devices that do a Fourier transform during operation here, maybe you'll find it interesting.)



Generally, anything that can be done after digitization digitally or before in analogue is usually done digitally — unless doing it in analogue reduces the digitization effort by magnitudes. Analogue electronics mostly have undesirable features for signal process – tolerances, non-linear in/out relationship that is hard to model, non-linear phases, temperature dependency, …



So, if you're in the business of processing signals, you typically want your signals to be digitized at the quality necessary, and afterwards do the math you want to do with the digital signal (which is actually numbers) instead of approximating the math you want to do with analogue components. You'll find that the same






share|improve this answer











$endgroup$












  • $begingroup$
    So the electronic recording pipeline is: signal->amplifier->AD converter->FFT chip->store as file. Then in software: read file->FFT transform? BTW the lecturer pointed out that recording in Fourier space was beneficial since the signal was highly compressed there.
    $endgroup$
    – Andy
    Mar 17 at 10:20


















2












$begingroup$

Maybe a place for some explanations.



There's no such thing as recording a radio signal in frequency domain. In receiver circuit only voltage as a function of time is available. There exists nothing else which could be amplified, stored or processed.



I guess you want to send frequency sweep bursts. Echos of them really can contain perfectly usable information, as good as a pulse radar could give with 1000 or 10000 times bigger transmitting power. It isn't only a guess. Because only quite low frequencies penetrate long distances into the soil, the used power must be limited to avoid problems with telecommunication companies. I bet pulse radars are out of the question for that reason.



Think a transmitted which sends with linearly growing frequency. The burst has constant power and it lasts until there's got echo signal from the most distant point under interest.



Receiving and detection: Transmitter output signal is splitted, about one milliwatt of it is directed to a mixer which gets the received signal simultaneously. Mixer output signal is amplified, stored and it's Fourier transform is calculated. The result IS the same as a pulse radar would give. That's easy to see, why. The mixer produces frequency components at differerence frequencies (the sum frequency components are filtered out). The frequency of an echo is the higher the further the echo had came from.



The actual circuitry: I have no ground radar circuit to give, so you must design it by yourself or buy it. I'm afraid to be able to do the design from scratch, you should have combined radio, analog electronics and digital signal processing knowledge total worth of 4 years of full time studies + excellent electronics development lab work skills. And of course, you need the lab.



You should get some realistic estimates how strong echo signals are available. Do not forget that the echos get weaker as the distance grows - that's because the soil attenuates and the signal spreads to wider area. If you are lucky, the transmitter can be weak enough to make possible simultaneous receiving without saturation and the most distant echos still are detectable under the noise. You need both measurements and math to find those basic limiting facts.



Probably you can also find them from published academic works. To be able to find and understand them the already mentioned knowledge is a must.



You can divide the design effort into few major areas:



1) finding the basic limits



2) overall system design



3) radio design (= finding numerical specs for transmitter, antenna, receiver and signal processing)



4) circuit design



5) signal processing programming



6) system control software programming



You were wondering, if RLC filter bank could do the signal processing job. In theory it can separate frequencies, but collecting the outputs is complex. Radar technology books contain practical solutions. Today Digital signal processing is the main route, but in the past various other methods were in use (=no fast enough computers available). Frequency dispersive surface acoustic filtering was one way to construct a pulse radar type output signal. The methods in general radar vocabulary were under term "pulse compression".



Get a ground penetrating radar technology book, find one which is understandable with your existing math, radio and electronics knowledge. If you collect more of that infrastructure knowledge, get higher level GPR technology book, too.






share|improve this answer











$endgroup$












  • $begingroup$
    Thank you! I do not understand why one need to mix the outgoing and received signal though? is this to get a reference for the phase of the incoming signal? Removing the incoming signal should be easy since it becomes a spike in the Fourier space?
    $endgroup$
    – Andy
    Mar 17 at 13:31










  • $begingroup$
    Implementing this with GPR is too ambitious for me, but I am considering implementing a similar experiment with audio using an arduino: arduino.stackexchange.com/questions/62630/…
    $endgroup$
    – Andy
    Mar 17 at 13:33










  • $begingroup$
    Ideal mixing = multiply two signals. Hopefully you can split with trigonometric formulas a product of 2 sinusoidals to a sum of 2 separate sinusoidals with frequencies= sum and difference. The difference of current output frequency and current echo signal frequency presents the distance. That's the reason to mix. It's mindless to think such as simply detecting a momentary frequency. No such measurable thing exists. When there's noise, you need a long sample to estimate its frequency. FFT is a good way to find the frequencies of several simultaneous sinusoidals.
    $endgroup$
    – user287001
    Mar 17 at 13:56











  • $begingroup$
    @Andy (continued) FFT is an computationally optimized method to calculate what a bank of narrowband BP filters would give. The calculated frequency components ARE time domain responses of such filters. You will see this in a second if you understand exactly signal math terms "time response calculation with convolution" and "Fourier transform"
    $endgroup$
    – user287001
    Mar 17 at 14:08











Your Answer





StackExchange.ifUsing("editor", function ()
return StackExchange.using("mathjaxEditing", function ()
StackExchange.MarkdownEditor.creationCallbacks.add(function (editor, postfix)
StackExchange.mathjaxEditing.prepareWmdForMathJax(editor, postfix, [["\$", "\$"]]);
);
);
, "mathjax-editing");

StackExchange.ifUsing("editor", function ()
return StackExchange.using("schematics", function ()
StackExchange.schematics.init();
);
, "cicuitlab");

StackExchange.ready(function()
var channelOptions =
tags: "".split(" "),
id: "135"
;
initTagRenderer("".split(" "), "".split(" "), channelOptions);

StackExchange.using("externalEditor", function()
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled)
StackExchange.using("snippets", function()
createEditor();
);

else
createEditor();

);

function createEditor()
StackExchange.prepareEditor(
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: false,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: null,
bindNavPrevention: true,
postfix: "",
imageUploader:
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
,
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
);



);













draft saved

draft discarded


















StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2felectronics.stackexchange.com%2fquestions%2f427629%2fhow-does-one-measure-the-fourier-components-of-a-signal%23new-answer', 'question_page');

);

Post as a guest















Required, but never shown

























2 Answers
2






active

oldest

votes








2 Answers
2






active

oldest

votes









active

oldest

votes






active

oldest

votes









7












$begingroup$


Is the signal split by many narrow band pass filters in paralell? Are these filters RLC circuits? Are there chips that contains many such filters in paralell?




No, these are typically collected as time signal, and then transformed digitally (by a DFT implemented by an FFT) to discrete Fourier domain for processing reasons.



In the sense that yes, there's many hardware implementations of FFTs, and the FFT can be considered as polyphase boxcar filterbank, there's chips containing a filterbanks – but they're processing a digitized time signal, not an analogue one.



(The reasons being pulse compression, mainly, i.e. you need to do a huge correlation with the known signal to even see your signal from the noise – there isn't exactly much signal coming back to the satellite after being radiated from lower earth orbit to ground, through the ground, scattered there, and back. Correllation is very computationally intense, as it goes with the square of the length of the signal, but with processing in frequency domain, you can significantly reduce that amount, and bonus, the data might become way easier to compress, store and transmit to the ground station.)



(By the way, I wrote a top-of-my-head list of devices that do a Fourier transform during operation here, maybe you'll find it interesting.)



Generally, anything that can be done after digitization digitally or before in analogue is usually done digitally — unless doing it in analogue reduces the digitization effort by magnitudes. Analogue electronics mostly have undesirable features for signal process – tolerances, non-linear in/out relationship that is hard to model, non-linear phases, temperature dependency, …



So, if you're in the business of processing signals, you typically want your signals to be digitized at the quality necessary, and afterwards do the math you want to do with the digital signal (which is actually numbers) instead of approximating the math you want to do with analogue components. You'll find that the same






share|improve this answer











$endgroup$












  • $begingroup$
    So the electronic recording pipeline is: signal->amplifier->AD converter->FFT chip->store as file. Then in software: read file->FFT transform? BTW the lecturer pointed out that recording in Fourier space was beneficial since the signal was highly compressed there.
    $endgroup$
    – Andy
    Mar 17 at 10:20















7












$begingroup$


Is the signal split by many narrow band pass filters in paralell? Are these filters RLC circuits? Are there chips that contains many such filters in paralell?




No, these are typically collected as time signal, and then transformed digitally (by a DFT implemented by an FFT) to discrete Fourier domain for processing reasons.



In the sense that yes, there's many hardware implementations of FFTs, and the FFT can be considered as polyphase boxcar filterbank, there's chips containing a filterbanks – but they're processing a digitized time signal, not an analogue one.



(The reasons being pulse compression, mainly, i.e. you need to do a huge correlation with the known signal to even see your signal from the noise – there isn't exactly much signal coming back to the satellite after being radiated from lower earth orbit to ground, through the ground, scattered there, and back. Correllation is very computationally intense, as it goes with the square of the length of the signal, but with processing in frequency domain, you can significantly reduce that amount, and bonus, the data might become way easier to compress, store and transmit to the ground station.)



(By the way, I wrote a top-of-my-head list of devices that do a Fourier transform during operation here, maybe you'll find it interesting.)



Generally, anything that can be done after digitization digitally or before in analogue is usually done digitally — unless doing it in analogue reduces the digitization effort by magnitudes. Analogue electronics mostly have undesirable features for signal process – tolerances, non-linear in/out relationship that is hard to model, non-linear phases, temperature dependency, …



So, if you're in the business of processing signals, you typically want your signals to be digitized at the quality necessary, and afterwards do the math you want to do with the digital signal (which is actually numbers) instead of approximating the math you want to do with analogue components. You'll find that the same






share|improve this answer











$endgroup$












  • $begingroup$
    So the electronic recording pipeline is: signal->amplifier->AD converter->FFT chip->store as file. Then in software: read file->FFT transform? BTW the lecturer pointed out that recording in Fourier space was beneficial since the signal was highly compressed there.
    $endgroup$
    – Andy
    Mar 17 at 10:20













7












7








7





$begingroup$


Is the signal split by many narrow band pass filters in paralell? Are these filters RLC circuits? Are there chips that contains many such filters in paralell?




No, these are typically collected as time signal, and then transformed digitally (by a DFT implemented by an FFT) to discrete Fourier domain for processing reasons.



In the sense that yes, there's many hardware implementations of FFTs, and the FFT can be considered as polyphase boxcar filterbank, there's chips containing a filterbanks – but they're processing a digitized time signal, not an analogue one.



(The reasons being pulse compression, mainly, i.e. you need to do a huge correlation with the known signal to even see your signal from the noise – there isn't exactly much signal coming back to the satellite after being radiated from lower earth orbit to ground, through the ground, scattered there, and back. Correllation is very computationally intense, as it goes with the square of the length of the signal, but with processing in frequency domain, you can significantly reduce that amount, and bonus, the data might become way easier to compress, store and transmit to the ground station.)



(By the way, I wrote a top-of-my-head list of devices that do a Fourier transform during operation here, maybe you'll find it interesting.)



Generally, anything that can be done after digitization digitally or before in analogue is usually done digitally — unless doing it in analogue reduces the digitization effort by magnitudes. Analogue electronics mostly have undesirable features for signal process – tolerances, non-linear in/out relationship that is hard to model, non-linear phases, temperature dependency, …



So, if you're in the business of processing signals, you typically want your signals to be digitized at the quality necessary, and afterwards do the math you want to do with the digital signal (which is actually numbers) instead of approximating the math you want to do with analogue components. You'll find that the same






share|improve this answer











$endgroup$




Is the signal split by many narrow band pass filters in paralell? Are these filters RLC circuits? Are there chips that contains many such filters in paralell?




No, these are typically collected as time signal, and then transformed digitally (by a DFT implemented by an FFT) to discrete Fourier domain for processing reasons.



In the sense that yes, there's many hardware implementations of FFTs, and the FFT can be considered as polyphase boxcar filterbank, there's chips containing a filterbanks – but they're processing a digitized time signal, not an analogue one.



(The reasons being pulse compression, mainly, i.e. you need to do a huge correlation with the known signal to even see your signal from the noise – there isn't exactly much signal coming back to the satellite after being radiated from lower earth orbit to ground, through the ground, scattered there, and back. Correllation is very computationally intense, as it goes with the square of the length of the signal, but with processing in frequency domain, you can significantly reduce that amount, and bonus, the data might become way easier to compress, store and transmit to the ground station.)



(By the way, I wrote a top-of-my-head list of devices that do a Fourier transform during operation here, maybe you'll find it interesting.)



Generally, anything that can be done after digitization digitally or before in analogue is usually done digitally — unless doing it in analogue reduces the digitization effort by magnitudes. Analogue electronics mostly have undesirable features for signal process – tolerances, non-linear in/out relationship that is hard to model, non-linear phases, temperature dependency, …



So, if you're in the business of processing signals, you typically want your signals to be digitized at the quality necessary, and afterwards do the math you want to do with the digital signal (which is actually numbers) instead of approximating the math you want to do with analogue components. You'll find that the same







share|improve this answer














share|improve this answer



share|improve this answer








edited Mar 17 at 10:11

























answered Mar 17 at 10:05









Marcus MüllerMarcus Müller

35.2k362101




35.2k362101











  • $begingroup$
    So the electronic recording pipeline is: signal->amplifier->AD converter->FFT chip->store as file. Then in software: read file->FFT transform? BTW the lecturer pointed out that recording in Fourier space was beneficial since the signal was highly compressed there.
    $endgroup$
    – Andy
    Mar 17 at 10:20
















  • $begingroup$
    So the electronic recording pipeline is: signal->amplifier->AD converter->FFT chip->store as file. Then in software: read file->FFT transform? BTW the lecturer pointed out that recording in Fourier space was beneficial since the signal was highly compressed there.
    $endgroup$
    – Andy
    Mar 17 at 10:20















$begingroup$
So the electronic recording pipeline is: signal->amplifier->AD converter->FFT chip->store as file. Then in software: read file->FFT transform? BTW the lecturer pointed out that recording in Fourier space was beneficial since the signal was highly compressed there.
$endgroup$
– Andy
Mar 17 at 10:20




$begingroup$
So the electronic recording pipeline is: signal->amplifier->AD converter->FFT chip->store as file. Then in software: read file->FFT transform? BTW the lecturer pointed out that recording in Fourier space was beneficial since the signal was highly compressed there.
$endgroup$
– Andy
Mar 17 at 10:20













2












$begingroup$

Maybe a place for some explanations.



There's no such thing as recording a radio signal in frequency domain. In receiver circuit only voltage as a function of time is available. There exists nothing else which could be amplified, stored or processed.



I guess you want to send frequency sweep bursts. Echos of them really can contain perfectly usable information, as good as a pulse radar could give with 1000 or 10000 times bigger transmitting power. It isn't only a guess. Because only quite low frequencies penetrate long distances into the soil, the used power must be limited to avoid problems with telecommunication companies. I bet pulse radars are out of the question for that reason.



Think a transmitted which sends with linearly growing frequency. The burst has constant power and it lasts until there's got echo signal from the most distant point under interest.



Receiving and detection: Transmitter output signal is splitted, about one milliwatt of it is directed to a mixer which gets the received signal simultaneously. Mixer output signal is amplified, stored and it's Fourier transform is calculated. The result IS the same as a pulse radar would give. That's easy to see, why. The mixer produces frequency components at differerence frequencies (the sum frequency components are filtered out). The frequency of an echo is the higher the further the echo had came from.



The actual circuitry: I have no ground radar circuit to give, so you must design it by yourself or buy it. I'm afraid to be able to do the design from scratch, you should have combined radio, analog electronics and digital signal processing knowledge total worth of 4 years of full time studies + excellent electronics development lab work skills. And of course, you need the lab.



You should get some realistic estimates how strong echo signals are available. Do not forget that the echos get weaker as the distance grows - that's because the soil attenuates and the signal spreads to wider area. If you are lucky, the transmitter can be weak enough to make possible simultaneous receiving without saturation and the most distant echos still are detectable under the noise. You need both measurements and math to find those basic limiting facts.



Probably you can also find them from published academic works. To be able to find and understand them the already mentioned knowledge is a must.



You can divide the design effort into few major areas:



1) finding the basic limits



2) overall system design



3) radio design (= finding numerical specs for transmitter, antenna, receiver and signal processing)



4) circuit design



5) signal processing programming



6) system control software programming



You were wondering, if RLC filter bank could do the signal processing job. In theory it can separate frequencies, but collecting the outputs is complex. Radar technology books contain practical solutions. Today Digital signal processing is the main route, but in the past various other methods were in use (=no fast enough computers available). Frequency dispersive surface acoustic filtering was one way to construct a pulse radar type output signal. The methods in general radar vocabulary were under term "pulse compression".



Get a ground penetrating radar technology book, find one which is understandable with your existing math, radio and electronics knowledge. If you collect more of that infrastructure knowledge, get higher level GPR technology book, too.






share|improve this answer











$endgroup$












  • $begingroup$
    Thank you! I do not understand why one need to mix the outgoing and received signal though? is this to get a reference for the phase of the incoming signal? Removing the incoming signal should be easy since it becomes a spike in the Fourier space?
    $endgroup$
    – Andy
    Mar 17 at 13:31










  • $begingroup$
    Implementing this with GPR is too ambitious for me, but I am considering implementing a similar experiment with audio using an arduino: arduino.stackexchange.com/questions/62630/…
    $endgroup$
    – Andy
    Mar 17 at 13:33










  • $begingroup$
    Ideal mixing = multiply two signals. Hopefully you can split with trigonometric formulas a product of 2 sinusoidals to a sum of 2 separate sinusoidals with frequencies= sum and difference. The difference of current output frequency and current echo signal frequency presents the distance. That's the reason to mix. It's mindless to think such as simply detecting a momentary frequency. No such measurable thing exists. When there's noise, you need a long sample to estimate its frequency. FFT is a good way to find the frequencies of several simultaneous sinusoidals.
    $endgroup$
    – user287001
    Mar 17 at 13:56











  • $begingroup$
    @Andy (continued) FFT is an computationally optimized method to calculate what a bank of narrowband BP filters would give. The calculated frequency components ARE time domain responses of such filters. You will see this in a second if you understand exactly signal math terms "time response calculation with convolution" and "Fourier transform"
    $endgroup$
    – user287001
    Mar 17 at 14:08
















2












$begingroup$

Maybe a place for some explanations.



There's no such thing as recording a radio signal in frequency domain. In receiver circuit only voltage as a function of time is available. There exists nothing else which could be amplified, stored or processed.



I guess you want to send frequency sweep bursts. Echos of them really can contain perfectly usable information, as good as a pulse radar could give with 1000 or 10000 times bigger transmitting power. It isn't only a guess. Because only quite low frequencies penetrate long distances into the soil, the used power must be limited to avoid problems with telecommunication companies. I bet pulse radars are out of the question for that reason.



Think a transmitted which sends with linearly growing frequency. The burst has constant power and it lasts until there's got echo signal from the most distant point under interest.



Receiving and detection: Transmitter output signal is splitted, about one milliwatt of it is directed to a mixer which gets the received signal simultaneously. Mixer output signal is amplified, stored and it's Fourier transform is calculated. The result IS the same as a pulse radar would give. That's easy to see, why. The mixer produces frequency components at differerence frequencies (the sum frequency components are filtered out). The frequency of an echo is the higher the further the echo had came from.



The actual circuitry: I have no ground radar circuit to give, so you must design it by yourself or buy it. I'm afraid to be able to do the design from scratch, you should have combined radio, analog electronics and digital signal processing knowledge total worth of 4 years of full time studies + excellent electronics development lab work skills. And of course, you need the lab.



You should get some realistic estimates how strong echo signals are available. Do not forget that the echos get weaker as the distance grows - that's because the soil attenuates and the signal spreads to wider area. If you are lucky, the transmitter can be weak enough to make possible simultaneous receiving without saturation and the most distant echos still are detectable under the noise. You need both measurements and math to find those basic limiting facts.



Probably you can also find them from published academic works. To be able to find and understand them the already mentioned knowledge is a must.



You can divide the design effort into few major areas:



1) finding the basic limits



2) overall system design



3) radio design (= finding numerical specs for transmitter, antenna, receiver and signal processing)



4) circuit design



5) signal processing programming



6) system control software programming



You were wondering, if RLC filter bank could do the signal processing job. In theory it can separate frequencies, but collecting the outputs is complex. Radar technology books contain practical solutions. Today Digital signal processing is the main route, but in the past various other methods were in use (=no fast enough computers available). Frequency dispersive surface acoustic filtering was one way to construct a pulse radar type output signal. The methods in general radar vocabulary were under term "pulse compression".



Get a ground penetrating radar technology book, find one which is understandable with your existing math, radio and electronics knowledge. If you collect more of that infrastructure knowledge, get higher level GPR technology book, too.






share|improve this answer











$endgroup$












  • $begingroup$
    Thank you! I do not understand why one need to mix the outgoing and received signal though? is this to get a reference for the phase of the incoming signal? Removing the incoming signal should be easy since it becomes a spike in the Fourier space?
    $endgroup$
    – Andy
    Mar 17 at 13:31










  • $begingroup$
    Implementing this with GPR is too ambitious for me, but I am considering implementing a similar experiment with audio using an arduino: arduino.stackexchange.com/questions/62630/…
    $endgroup$
    – Andy
    Mar 17 at 13:33










  • $begingroup$
    Ideal mixing = multiply two signals. Hopefully you can split with trigonometric formulas a product of 2 sinusoidals to a sum of 2 separate sinusoidals with frequencies= sum and difference. The difference of current output frequency and current echo signal frequency presents the distance. That's the reason to mix. It's mindless to think such as simply detecting a momentary frequency. No such measurable thing exists. When there's noise, you need a long sample to estimate its frequency. FFT is a good way to find the frequencies of several simultaneous sinusoidals.
    $endgroup$
    – user287001
    Mar 17 at 13:56











  • $begingroup$
    @Andy (continued) FFT is an computationally optimized method to calculate what a bank of narrowband BP filters would give. The calculated frequency components ARE time domain responses of such filters. You will see this in a second if you understand exactly signal math terms "time response calculation with convolution" and "Fourier transform"
    $endgroup$
    – user287001
    Mar 17 at 14:08














2












2








2





$begingroup$

Maybe a place for some explanations.



There's no such thing as recording a radio signal in frequency domain. In receiver circuit only voltage as a function of time is available. There exists nothing else which could be amplified, stored or processed.



I guess you want to send frequency sweep bursts. Echos of them really can contain perfectly usable information, as good as a pulse radar could give with 1000 or 10000 times bigger transmitting power. It isn't only a guess. Because only quite low frequencies penetrate long distances into the soil, the used power must be limited to avoid problems with telecommunication companies. I bet pulse radars are out of the question for that reason.



Think a transmitted which sends with linearly growing frequency. The burst has constant power and it lasts until there's got echo signal from the most distant point under interest.



Receiving and detection: Transmitter output signal is splitted, about one milliwatt of it is directed to a mixer which gets the received signal simultaneously. Mixer output signal is amplified, stored and it's Fourier transform is calculated. The result IS the same as a pulse radar would give. That's easy to see, why. The mixer produces frequency components at differerence frequencies (the sum frequency components are filtered out). The frequency of an echo is the higher the further the echo had came from.



The actual circuitry: I have no ground radar circuit to give, so you must design it by yourself or buy it. I'm afraid to be able to do the design from scratch, you should have combined radio, analog electronics and digital signal processing knowledge total worth of 4 years of full time studies + excellent electronics development lab work skills. And of course, you need the lab.



You should get some realistic estimates how strong echo signals are available. Do not forget that the echos get weaker as the distance grows - that's because the soil attenuates and the signal spreads to wider area. If you are lucky, the transmitter can be weak enough to make possible simultaneous receiving without saturation and the most distant echos still are detectable under the noise. You need both measurements and math to find those basic limiting facts.



Probably you can also find them from published academic works. To be able to find and understand them the already mentioned knowledge is a must.



You can divide the design effort into few major areas:



1) finding the basic limits



2) overall system design



3) radio design (= finding numerical specs for transmitter, antenna, receiver and signal processing)



4) circuit design



5) signal processing programming



6) system control software programming



You were wondering, if RLC filter bank could do the signal processing job. In theory it can separate frequencies, but collecting the outputs is complex. Radar technology books contain practical solutions. Today Digital signal processing is the main route, but in the past various other methods were in use (=no fast enough computers available). Frequency dispersive surface acoustic filtering was one way to construct a pulse radar type output signal. The methods in general radar vocabulary were under term "pulse compression".



Get a ground penetrating radar technology book, find one which is understandable with your existing math, radio and electronics knowledge. If you collect more of that infrastructure knowledge, get higher level GPR technology book, too.






share|improve this answer











$endgroup$



Maybe a place for some explanations.



There's no such thing as recording a radio signal in frequency domain. In receiver circuit only voltage as a function of time is available. There exists nothing else which could be amplified, stored or processed.



I guess you want to send frequency sweep bursts. Echos of them really can contain perfectly usable information, as good as a pulse radar could give with 1000 or 10000 times bigger transmitting power. It isn't only a guess. Because only quite low frequencies penetrate long distances into the soil, the used power must be limited to avoid problems with telecommunication companies. I bet pulse radars are out of the question for that reason.



Think a transmitted which sends with linearly growing frequency. The burst has constant power and it lasts until there's got echo signal from the most distant point under interest.



Receiving and detection: Transmitter output signal is splitted, about one milliwatt of it is directed to a mixer which gets the received signal simultaneously. Mixer output signal is amplified, stored and it's Fourier transform is calculated. The result IS the same as a pulse radar would give. That's easy to see, why. The mixer produces frequency components at differerence frequencies (the sum frequency components are filtered out). The frequency of an echo is the higher the further the echo had came from.



The actual circuitry: I have no ground radar circuit to give, so you must design it by yourself or buy it. I'm afraid to be able to do the design from scratch, you should have combined radio, analog electronics and digital signal processing knowledge total worth of 4 years of full time studies + excellent electronics development lab work skills. And of course, you need the lab.



You should get some realistic estimates how strong echo signals are available. Do not forget that the echos get weaker as the distance grows - that's because the soil attenuates and the signal spreads to wider area. If you are lucky, the transmitter can be weak enough to make possible simultaneous receiving without saturation and the most distant echos still are detectable under the noise. You need both measurements and math to find those basic limiting facts.



Probably you can also find them from published academic works. To be able to find and understand them the already mentioned knowledge is a must.



You can divide the design effort into few major areas:



1) finding the basic limits



2) overall system design



3) radio design (= finding numerical specs for transmitter, antenna, receiver and signal processing)



4) circuit design



5) signal processing programming



6) system control software programming



You were wondering, if RLC filter bank could do the signal processing job. In theory it can separate frequencies, but collecting the outputs is complex. Radar technology books contain practical solutions. Today Digital signal processing is the main route, but in the past various other methods were in use (=no fast enough computers available). Frequency dispersive surface acoustic filtering was one way to construct a pulse radar type output signal. The methods in general radar vocabulary were under term "pulse compression".



Get a ground penetrating radar technology book, find one which is understandable with your existing math, radio and electronics knowledge. If you collect more of that infrastructure knowledge, get higher level GPR technology book, too.







share|improve this answer














share|improve this answer



share|improve this answer








edited Mar 17 at 13:13

























answered Mar 17 at 12:35









user287001user287001

9,5691517




9,5691517











  • $begingroup$
    Thank you! I do not understand why one need to mix the outgoing and received signal though? is this to get a reference for the phase of the incoming signal? Removing the incoming signal should be easy since it becomes a spike in the Fourier space?
    $endgroup$
    – Andy
    Mar 17 at 13:31










  • $begingroup$
    Implementing this with GPR is too ambitious for me, but I am considering implementing a similar experiment with audio using an arduino: arduino.stackexchange.com/questions/62630/…
    $endgroup$
    – Andy
    Mar 17 at 13:33










  • $begingroup$
    Ideal mixing = multiply two signals. Hopefully you can split with trigonometric formulas a product of 2 sinusoidals to a sum of 2 separate sinusoidals with frequencies= sum and difference. The difference of current output frequency and current echo signal frequency presents the distance. That's the reason to mix. It's mindless to think such as simply detecting a momentary frequency. No such measurable thing exists. When there's noise, you need a long sample to estimate its frequency. FFT is a good way to find the frequencies of several simultaneous sinusoidals.
    $endgroup$
    – user287001
    Mar 17 at 13:56











  • $begingroup$
    @Andy (continued) FFT is an computationally optimized method to calculate what a bank of narrowband BP filters would give. The calculated frequency components ARE time domain responses of such filters. You will see this in a second if you understand exactly signal math terms "time response calculation with convolution" and "Fourier transform"
    $endgroup$
    – user287001
    Mar 17 at 14:08

















  • $begingroup$
    Thank you! I do not understand why one need to mix the outgoing and received signal though? is this to get a reference for the phase of the incoming signal? Removing the incoming signal should be easy since it becomes a spike in the Fourier space?
    $endgroup$
    – Andy
    Mar 17 at 13:31










  • $begingroup$
    Implementing this with GPR is too ambitious for me, but I am considering implementing a similar experiment with audio using an arduino: arduino.stackexchange.com/questions/62630/…
    $endgroup$
    – Andy
    Mar 17 at 13:33










  • $begingroup$
    Ideal mixing = multiply two signals. Hopefully you can split with trigonometric formulas a product of 2 sinusoidals to a sum of 2 separate sinusoidals with frequencies= sum and difference. The difference of current output frequency and current echo signal frequency presents the distance. That's the reason to mix. It's mindless to think such as simply detecting a momentary frequency. No such measurable thing exists. When there's noise, you need a long sample to estimate its frequency. FFT is a good way to find the frequencies of several simultaneous sinusoidals.
    $endgroup$
    – user287001
    Mar 17 at 13:56











  • $begingroup$
    @Andy (continued) FFT is an computationally optimized method to calculate what a bank of narrowband BP filters would give. The calculated frequency components ARE time domain responses of such filters. You will see this in a second if you understand exactly signal math terms "time response calculation with convolution" and "Fourier transform"
    $endgroup$
    – user287001
    Mar 17 at 14:08
















$begingroup$
Thank you! I do not understand why one need to mix the outgoing and received signal though? is this to get a reference for the phase of the incoming signal? Removing the incoming signal should be easy since it becomes a spike in the Fourier space?
$endgroup$
– Andy
Mar 17 at 13:31




$begingroup$
Thank you! I do not understand why one need to mix the outgoing and received signal though? is this to get a reference for the phase of the incoming signal? Removing the incoming signal should be easy since it becomes a spike in the Fourier space?
$endgroup$
– Andy
Mar 17 at 13:31












$begingroup$
Implementing this with GPR is too ambitious for me, but I am considering implementing a similar experiment with audio using an arduino: arduino.stackexchange.com/questions/62630/…
$endgroup$
– Andy
Mar 17 at 13:33




$begingroup$
Implementing this with GPR is too ambitious for me, but I am considering implementing a similar experiment with audio using an arduino: arduino.stackexchange.com/questions/62630/…
$endgroup$
– Andy
Mar 17 at 13:33












$begingroup$
Ideal mixing = multiply two signals. Hopefully you can split with trigonometric formulas a product of 2 sinusoidals to a sum of 2 separate sinusoidals with frequencies= sum and difference. The difference of current output frequency and current echo signal frequency presents the distance. That's the reason to mix. It's mindless to think such as simply detecting a momentary frequency. No such measurable thing exists. When there's noise, you need a long sample to estimate its frequency. FFT is a good way to find the frequencies of several simultaneous sinusoidals.
$endgroup$
– user287001
Mar 17 at 13:56





$begingroup$
Ideal mixing = multiply two signals. Hopefully you can split with trigonometric formulas a product of 2 sinusoidals to a sum of 2 separate sinusoidals with frequencies= sum and difference. The difference of current output frequency and current echo signal frequency presents the distance. That's the reason to mix. It's mindless to think such as simply detecting a momentary frequency. No such measurable thing exists. When there's noise, you need a long sample to estimate its frequency. FFT is a good way to find the frequencies of several simultaneous sinusoidals.
$endgroup$
– user287001
Mar 17 at 13:56













$begingroup$
@Andy (continued) FFT is an computationally optimized method to calculate what a bank of narrowband BP filters would give. The calculated frequency components ARE time domain responses of such filters. You will see this in a second if you understand exactly signal math terms "time response calculation with convolution" and "Fourier transform"
$endgroup$
– user287001
Mar 17 at 14:08





$begingroup$
@Andy (continued) FFT is an computationally optimized method to calculate what a bank of narrowband BP filters would give. The calculated frequency components ARE time domain responses of such filters. You will see this in a second if you understand exactly signal math terms "time response calculation with convolution" and "Fourier transform"
$endgroup$
– user287001
Mar 17 at 14:08


















draft saved

draft discarded
















































Thanks for contributing an answer to Electrical Engineering Stack Exchange!


  • Please be sure to answer the question. Provide details and share your research!

But avoid


  • Asking for help, clarification, or responding to other answers.

  • Making statements based on opinion; back them up with references or personal experience.

Use MathJax to format equations. MathJax reference.


To learn more, see our tips on writing great answers.




draft saved


draft discarded














StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2felectronics.stackexchange.com%2fquestions%2f427629%2fhow-does-one-measure-the-fourier-components-of-a-signal%23new-answer', 'question_page');

);

Post as a guest















Required, but never shown





















































Required, but never shown














Required, but never shown












Required, but never shown







Required, but never shown

































Required, but never shown














Required, but never shown












Required, but never shown







Required, but never shown







Popular posts from this blog

Lowndes Grove History Architecture References Navigation menu32°48′6″N 79°57′58″W / 32.80167°N 79.96611°W / 32.80167; -79.9661132°48′6″N 79°57′58″W / 32.80167°N 79.96611°W / 32.80167; -79.9661178002500"National Register Information System"Historic houses of South Carolina"Lowndes Grove""+32° 48' 6.00", −79° 57' 58.00""Lowndes Grove, Charleston County (260 St. Margaret St., Charleston)""Lowndes Grove"The Charleston ExpositionIt Happened in South Carolina"Lowndes Grove (House), Saint Margaret Street & Sixth Avenue, Charleston, Charleston County, SC(Photographs)"Plantations of the Carolina Low Countrye

random experiment with two different functions on unit interval Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 23, 2019 at 00:00UTC (8:00pm US/Eastern)Random variable and probability space notionsRandom Walk with EdgesFinding functions where the increase over a random interval is Poisson distributedNumber of days until dayCan an observed event in fact be of zero probability?Unit random processmodels of coins and uniform distributionHow to get the number of successes given $n$ trials , probability $P$ and a random variable $X$Absorbing Markov chain in a computer. Is “almost every” turned into always convergence in computer executions?Stopped random walk is not uniformly integrable

How should I support this large drywall patch? Planned maintenance scheduled April 23, 2019 at 00:00UTC (8:00pm US/Eastern) Announcing the arrival of Valued Associate #679: Cesar Manara Unicorn Meta Zoo #1: Why another podcast?How do I cover large gaps in drywall?How do I keep drywall around a patch from crumbling?Can I glue a second layer of drywall?How to patch long strip on drywall?Large drywall patch: how to avoid bulging seams?Drywall Mesh Patch vs. Bulge? To remove or not to remove?How to fix this drywall job?Prep drywall before backsplashWhat's the best way to fix this horrible drywall patch job?Drywall patching using 3M Patch Plus Primer