When it comes to interpreting sensor data automatically, it helps to have a large data set to assist in validating it, as well as training when it concerns machine learning (ML). Creating this data set with carefully tagged and categorized information is a long and tedious process, which is where the idea of cross-domain translations come into play, as in the case of using millimeter wave (mmWave) radar sensors to recognize activity of e.g. building occupants with the IMU2Doppler project at Smash Lab of Carnegie Mellon University.
The most commonly used sensor type when it comes to classifying especially human motion are inertial measurement units (IMU) such as accelerometers and gyroscopes, which are found in everything from smartphones to smart watches and fitness bands. For these devices it’s common to classify measurement patterns as matches a particular activity, such as walking, jogging, or brushing one’s teeth. This makes them both well-defined and very accessible.
As for why a mmWave-based Doppler radar would be preferred for monitoring e.g. building occupants is the privacy aspect compared to using cameras, and the inconvenience of equipping people with a body-worn IMU. Using Doppler radar it would theoretically be possible for people to track activities within their own home, as well as in a medical setting to ensure patients are safe, or at a gym to track one’s performance, or usage of equipment. All without the use of cameras or personal sensors. In the past, we’ve seen a similar approach that used targeted laser beams.
As promising as this sounds, at this point in time the number of activities that are recognized with reasonable accuracy (~70%) is limited to ten types. Depending on the intended application this may already be sufficient, though as the published paper notes, there is still a lot of room for growth.
Millimeter-wave Radars used in modern cars for cruise control and collision avoidance are usually designed to work at ranges on the order of 100 meters or so. With some engineering nous, however, experimenters have gotten these devices sending signals over ranges of up to 60 km in some tests. [Machining and Microwaves] decided to see if he could push the boat out even further, and set out machining some waveguide combiner cavities so he could use the radar chips with some very high-performance antennas.
The end goal of the project is to produce a 53 dBi antenna for the 122GHz signal put out by the mmWave radar chips commonly found in automotive applications. Working at this frequency requires getting tolerances just so in order to create an antenna that performs well.
Plenty of fine lathe work and cheerful machining banter later, and the precision waveguide is done. It may not look like much to the untrained eye, but much careful design and machining went on to make it both easy to attach to the radar and parabolic antenna system, and to make it perform at a high enough level to hopefully break records set by other enterprising radio experimenters. If that wasn’t all hard enough, though, the final job involved making 24 of these things!
There aren’t a whole lot of microwave antenna-specific machining channels on YouTube, so if you’ve been thirsty for that kind of content, this video is very much for you. If you’re more interested in antennas for lower frequencies, though, you might find some of our other stories to your liking. Video after the break.