At the start of this week, I finished up the slides for the design review. To fully flesh out my approach, I read a few articles to understand differences between different CNN architectures. I have worked with 2D CNN architectures before and am most familiar with them. However, since the range-Doppler maps that we will input are 3D, in the end, I found that using a 3D CNN architecture would be the most fitting for the task. I learned that 3D CNN’s differ in that the kernel slides in all three dimensions. After pinning down my architecture, it was fairly easy to find code that accomplished this network (I was unable to attach the Python file, but I did want to). However, it’s important that the parameters are relevant to the radar classification task. I found a very interesting research paper on exactly this topic, and I plan on reading it and changing the parameters of the network accordingly (currently the network is for classifying CT images). While I made progress on the architecture, I struggled with generating inputs from the dataset. We have the range-Azimuth and range-Doppler data, but from that a range-Doppler map needs to be constructed. Furthermore, the label for each training data piece will be a cubelet outlining the human in the frame (this is something I didn’t previously understand). Therefore, this will be a big focus for me moving forward–making both the heat maps and the cubelets. Lastly, I drafted the introduction and use case requirements sections of the design report. I also added relevant details about the ML architecture in the architecture, design requirements, design trade studies, and test, verification, and validation sections.
My progress is behind. I was hoping to get the network training by now, but the input and label generation was much more complicated than I expected. However, as long as I can get the network reliably training by spring break, I think we are on a good track for leaving an ample amount of time for integration.
By the end of this week, I hope to get the network training. The bulk of that work will be generating the inputs and the targets.
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