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
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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
$begingroup$
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?
high-frequency fourier radar
edited Mar 18 at 3:56
Mast
5421418
asked Mar 17 at 9:47
AndyAndy
1565
$endgroup$
add a comment |
$begingroup$
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?
high-frequency fourier radar
edited Mar 18 at 3:56
Mast
5421418
asked Mar 17 at 9:47
AndyAndy
1565
$endgroup$
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
add a comment |
$begingroup$
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?
high-frequency fourier radar
edited Mar 18 at 3:56
Mast
5421418
asked Mar 17 at 9:47
AndyAndy
1565
$endgroup$
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
high-frequency fourier radar
edited Mar 18 at 3:56
Mast
5421418
asked Mar 17 at 9:47
AndyAndy
1565
edited Mar 18 at 3:56
Mast
5421418
asked Mar 17 at 9:47
AndyAndy
1565
edited Mar 18 at 3:56
Mast
5421418
edited Mar 18 at 3:56
Mast
5421418
edited Mar 18 at 3:56
Mast
5421418
5421418
asked Mar 17 at 9:47
AndyAndy
1565
asked Mar 17 at 9:47
AndyAndy
1565
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
add a comment |
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
add a comment |
2 Answers
2
active
oldest
votes
$begingroup$
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
answered Mar 17 at 10:05
Marcus MüllerMarcus Müller
35.2k362101
$endgroup$
$begingroup$
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
add a comment |
$begingroup$
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.
answered Mar 17 at 12:35
user287001user287001
9,5691517
$endgroup$
$begingroup$
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
add a comment |
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2 Answers
2
active
oldest
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2 Answers
2
active
oldest
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active
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active
oldest
votes
$begingroup$
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
answered Mar 17 at 10:05
Marcus MüllerMarcus Müller
35.2k362101
$endgroup$
$begingroup$
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
add a comment |
$begingroup$
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
answered Mar 17 at 10:05
Marcus MüllerMarcus Müller
35.2k362101
$endgroup$
$begingroup$
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
add a comment |
$begingroup$
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
answered Mar 17 at 10:05
Marcus MüllerMarcus Müller
35.2k362101
$endgroup$
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
answered Mar 17 at 10:05
Marcus MüllerMarcus Müller
35.2k362101
edited Mar 17 at 10:11
answered Mar 17 at 10:05
Marcus MüllerMarcus Müller
35.2k362101
answered Mar 17 at 10:05
Marcus MüllerMarcus Müller
35.2k362101
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
add a comment |
$begingroup$
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
add a comment |
$begingroup$
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.
answered Mar 17 at 12:35
user287001user287001
9,5691517
$endgroup$
$begingroup$
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
add a comment |
$begingroup$
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.
answered Mar 17 at 12:35
user287001user287001
9,5691517
$endgroup$
$begingroup$
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
add a comment |
$begingroup$
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.
answered Mar 17 at 12:35
user287001user287001
9,5691517
$endgroup$
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.
answered Mar 17 at 12:35
user287001user287001
9,5691517
edited Mar 17 at 13:13
answered Mar 17 at 12:35
user287001user287001
9,5691517
answered Mar 17 at 12:35
user287001user287001
9,5691517
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
add a comment |
$begingroup$
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
add a comment |
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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