SAR

Beaver Works Synethic Aperture Radar

Over the summer of 2024 I coded a radar and detected landmines at MIT

Range-Time Intensity Plots

Over the summer of 2024, I had the opportunity to learn about and work with radars at MIT. The radar was a small ultra-wideband radar called the PulsON P440, and we communicated with it via Python scripts from a Raspberry Pi.

Figure 1-1. PulsON P440 with two log-periodic antennae

The project consisted of a few phases, the first of which was writing the python program to interact with the PulsON 440. The data gained from the radar was then plotted on a range-time intensity plot, with range on the x-axis and time on the y-axis.

Figure 1-2. Range-time intensity plot dipicting an an object that starts 60m away and moves to 50m over 3 seconds

Figure 1-3. Range-time intensity plot showing an object oscillating between 10m and 4m with a period of 3.5 seconds

We noticed it could be difficult to make out small objects or objects at far distances due to the radar's limited power, so we also created delta range-time intensity plots, which subtract two adjacent pulses to find the differences between them. Along with making motion easier to see, these plots had the added benefit of reducing the large initial pulse detected by the receiving antenna as the radar is transmitting.

Figure 1-4. Range-time intensity delta plot of Figure 1-2

Figure 1-5. Range-time intensity delta plot of Figure 1-3

Time Domain Backprojection

Once we had developed a method for plotting our radar returns, we focused on creating synthetic aperture radar maps. Synthetic aperture radar (SAR) is a technique used to increase resolution by overlaying multiple scans of the same target area. The same effect can be achieved by making your radar aperture far larger, so in some sense you're creating a synthetic aperture by moving the radar. To create our images, we flew the radar on drones in MIT's high bay. We then used a system of infrared tracking cameras to get position data on the drone, and lined it up with our scan. We then combined the motion capture and radar data to form our own maps, generally of aluminum soda cans placed on the floor.

Figure 2-1. PulsON 440 and Raspberry Pi assembly mounted below an F550 drone

Figure 2-2. Arrangement of soda cans in the high bay, with retroflectors as known reference points

The final challenge was to use this technique to discover the pattern of soda cans hidden beneath a tarp on the floor.

Figure 2-3. Initial image generated from the final challenge

Figure 2-4. Enhanced image, showing 24 cans arranged in a large "24"

Landmine Detection

In the last week of the program, we were introduced to one possible application of synthetic aperture radar: using it to detect buried landmines. Representatives from MIT's Lincoln Laboratory presented the topic and explained that they too had been assigned to a similar project.

To see if we could pick up the landmine, we went to one of the sand pits and set the radar up on a cart attached to a zipline. The cart would move across the wire at a constant speed, and had an encoder so we could know its position along the line. We then set up two refelectors, one in each corner of the sand pit, to act as references, and buried the landmine around one foot under the sand.

Figure 3-1. Sand pit and zipline setup

We did end up detecting the landmine. Figure 3-2 is a horizontal cross section of the sand pit, with the surface of the sand at 0m on the y-axis. The two reference reflectors are at 2m and 11m on the x-axis, and the landmine is clearly visible at 7m on the x-axis and about 0.5m under the surface.

Figure 3-2. SAR image of the sand pit

Of the five teams in the program, we were one of two that discovered the soda can pattern, and the only one to successfully detect the landmine.