Timeline for Find the Transfer Function from Magnitude and Phase Response
Current License: CC BY-SA 4.0
9 events
| when toggle format | what | by | license | comment | |
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| Apr 19, 2022 at 12:08 | vote | accept | Dan Boschen | ||
| Apr 19, 2022 at 10:17 | comment | added | Matt L. | @DanBoschen: I also added a design example with different desired phases for the same magnitude spec. | |
| Apr 19, 2022 at 10:15 | history | edited | Matt L. | CC BY-SA 4.0 |
added 862 characters in body
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| Apr 19, 2022 at 9:51 | comment | added | Matt L. | @DanBoschen: I think the main difference between the time domain and the frequency domain approaches is that in the frequency domain you could add a weighting function to give more or less emphasis to certain frequency regions. Of course, in the time domain you could weigh different time ranges differently, even though I don't think that this has much practical relevance. | |
| Apr 19, 2022 at 3:22 | comment | added | Dan Boschen | I like this answer and wanted to make sure you saw my comments/questions-- wasn't sure if I was supposed to @ you to see it when it's your own response | |
| Apr 18, 2022 at 12:10 | comment | added | Dan Boschen | I think the primary difference is to do that we would need to have the impulse response while you are solving the overdetermined set of equations directly in the freq domain but otherwise equivalent-- which then if getting the impulse response is done using DFT it would similar to the "Frequency Sampling" limitation affecting its accuracy, so we end up zero padding and using a much larger DFT to reduce those aliasing effects. Still I'd like to compare the two approaches in more detail if that hasn't already been done; is it something you ever considered? (time domain solution vs freq domain) | |
| Apr 18, 2022 at 12:04 | comment | added | Dan Boschen | (such as I explain in more detail here: dsp.stackexchange.com/questions/31318/… Is this basically your "system of equations to solve the least squares solution" or a different approach you didn't yet list? (As it is using the time domain response rather than the frequency domain, so I though it might be different) | |
| Apr 18, 2022 at 12:03 | comment | added | Dan Boschen | I was considering the many applications where we aren't using filters to select certain frequencies and block others (which "filter" very much implies!) but more impart a magnitude and frequency relationship across the entire bandwidth. Equalization comes to mind and I am familiar with the Wiener Hopf equations which serves to provide a least squares fit using the input and output waveforms as targets rather than the response--- but then we could use the impulse response itself in the time domain which serves the requirement of being spectrally rich-- how does that approach compare? | |
| Apr 18, 2022 at 11:46 | history | answered | Matt L. | CC BY-SA 4.0 |