Very cool! Six years ago I worked on a mmWave (76-81GHz) imaging radar with a Rotman lens Tx and Rx. Designed as a LiDAR replacement, but we could see pipes in walls, or detect concealed weapons at ~1km.
32 port Tx (vertical pancake beams) x 16 port Rx (horizontal pancake), something like 60 by 30 degrees. the entire thing used FPGA transceivers as one-bit DAC/ADC, Complementary Golay Code waveforms with one-bit correlation in the FPGAs (two VCU128s) -- digital logic was essentially the same as a binarized neural network, I squeezed a ton of popcnt performance out of those chips using both DSPs and LUTs
So thankful the author posted this. We often learn more from failure than success. Learning from the failures of others is how we can move forward.
The lessons learned at the bottom of the article are gold.
thank you so much for your feedback, it was hard to admit defeat, but at the end looking back at what I built, the parts where I learnt about RF, and just struggled, refactoring the code for the sim (thank god cc is not good enough to understand real world physics functionning for now) were the most satisfying moments
Very cool! Six years ago I worked on a mmWave (76-81GHz) imaging radar with a Rotman lens Tx and Rx. Designed as a LiDAR replacement, but we could see pipes in walls, or detect concealed weapons at ~1km.
Do you have a writeup about the project? I'd love to read more about it.
How many tx and rx antennas did you have ? (I don’t know if it was clear, my stack was 57-64 GHz, 2TX , 3RX)
32 port Tx (vertical pancake beams) x 16 port Rx (horizontal pancake), something like 60 by 30 degrees. the entire thing used FPGA transceivers as one-bit DAC/ADC, Complementary Golay Code waveforms with one-bit correlation in the FPGAs (two VCU128s) -- digital logic was essentially the same as a binarized neural network, I squeezed a ton of popcnt performance out of those chips using both DSPs and LUTs
love the background music in combination with the flying fishes wallpaper in the first video haha
very cool project
Hugged to death but I'd love to see this!
So thankful the author posted this. We often learn more from failure than success. Learning from the failures of others is how we can move forward. The lessons learned at the bottom of the article are gold.
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If you'd like to learn more about the module:
https://www.ti.com/tool/IWRL6432BOOST
thank you so much for your feedback, it was hard to admit defeat, but at the end looking back at what I built, the parts where I learnt about RF, and just struggled, refactoring the code for the sim (thank god cc is not good enough to understand real world physics functionning for now) were the most satisfying moments
My netlify crashed fixing the website rn
just fixed it, hope it works
Terrific project!
thanks :) !!
That's awesome. I built one for a capstone back in the day and know how tough it is to get onboarded. Kudos.