David Schneider describes his experience building a coffee-can radar based on a design by Greg Charvat of MIT’s Lincoln Laboratory:
A detailed description of the radar and how to build it is available at MIT’s open courseware website.
Having little experience with radio frequency circuitry, I worried that this project might be too challenging. Ironically, the RF section was the easiest part to construct. It merely required screwing together a few microwave components. And as long as you follow the prescription in the lecture notes exactly, you won’t need a network analyzer to match the antennas to the radar circuitry.
Most people’s mental picture of how radar operates is that the apparatus gives off a radio pulse and then waits to receive an echo, timing how long it takes to return. Dividing by the speed of light gives the round-trip distance to a target. Some radar sets do just that, but this one uses a different strategy: One antenna emits a continuous stream of waves while the other receives a continuous stream of echoes. The circuitry for this isn’t complicated, but interpreting the received signals requires some computational horsepower.
The key to this design is that the frequency of the outgoing radio waves increases linearly over time (for a short period, after which the cycle repeats), so the frequency of the reflected waves also increases linearly. But the reflected waves return to the receiving antenna after a short delay, by which time the waves being emitted are at a slightly higher frequency. The farther away the target, the greater the difference between these two frequencies.
To measure this difference, you use what radio engineers call a mixer, which here generates an output signal containing two new frequencies that are the sum and difference of the transmitted and received frequencies. Only the difference matters for this application, so the radar circuitry filters out the high frequencies, including the sum, and amplifies what’s left. This final signal is in the audio frequency range and can easily be recorded using a computer’s sound card—much more practical than trying to build a system that works directly with microwave frequency signals throughout.
I first set up the radar next to my garage and recorded about a half minute of data as I ran up and down the driveway. I captured that data with Audacity, a free audio editor, running on an old desktop PC that had a sound card with a line-in port. I analyzed the recording using a Matlab script provided by the instructors at MIT. Running the script proved a challenge, though, because Matlab was too pricey for my shoestring budget. But I found a free open-source alternative that served as a reasonable stand-in: Octave.
It took about 4 minutes to process the data, but it was worth the wait: The script transformed subtle changes in the audio signal into a zigzag plot that matched my back-and-forth movements. Wow!