Timeline for Time Alignment of 2 Sensors Sampling the Same Signal with Different Hardware Delays
Current License: CC BY-SA 4.0
8 events
| when toggle format | what | by | license | comment | |
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| Jul 13, 2018 at 12:46 | comment | added | jstein123 | Oh ok that makes sense! Do you know of any envelope finding algorithms that work very well on very noisy high resolution radio samples? Nothing I tried (hilbert transforms, etc) worked well at all. Even the rolling max doesn't work so great when the amplitude of the silence region isn't so much smaller than the amplitude of the noise region. Thanks! | |
| Jul 12, 2018 at 1:31 | comment | added | robert bristow-johnson | "I'm taking the rolling max of the magnitudes of the signal with windows of 1000..." @jstein123, that is (also) an envelope. | |
| Jul 9, 2018 at 21:23 | comment | added | jstein123 | Just edited the post to include links to 2 signals (I picked tricky ones). They were each recorded at almost the exact same time (each from a different radio), but there is a slight delay. Let me know if you're able to get cross correlation working, thanks! | |
| Jul 9, 2018 at 20:31 | comment | added | Royi | If you post a link to the 2 signals we can try demonstrate something. | |
| Jul 9, 2018 at 19:56 | vote | accept | jstein123 | ||
| Jul 9, 2018 at 19:56 | comment | added | jstein123 | Thanks! I'm taking the rolling max of the magnitudes of the signal with windows of 1000, then I'm setting all the values under a threshold to a very low number, then I'm taking the gradient of that to find the starts and ends of the signal regions. I'm able to get a near-perfect alignment for most of them with this method. However, it bugs me a bit that I'm still not able to get cross correlation working. I normalized both signals by dividing them by their max, but the cross correlation still has no local max at the correct alignment. Thanks for your help! | |
| Jul 9, 2018 at 17:30 | history | edited | Royi | CC BY-SA 4.0 |
added 175 characters in body
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| Jul 9, 2018 at 16:44 | history | answered | Royi | CC BY-SA 4.0 |