7 Platform one Uncrewed Systems Technology | April/May 2025 A new low power sensing technique allows a UAV to determine its position, in indoor, dark, and low-visibility environments for autonomous navigation writes Nick Flaherty. The MiFLy system developed at the Massachusetts Institute of Technology (MIT) uses millimetre wave radio at 28 GHz reflected by a single tag placed in its environment to autonomously self-localize. Because mifly enables self-localization with only one small tag, which could be affixed to a wall like a sticker, it would be cheaper and easier to implement than systems that require multiple tags. In addition, since the mifly tag reflects signals sent by the UAV, rather than generating its own signal, it can be operated with very low power. Two off-the-shelf radar sensors mounted on the UAV enable it to localize by measuring the reflections from the tag. Those measurements are fused with data from the onboard computer, which enables it to estimate its trajectory. The researchers conducted hundreds of flight experiments with UAVs in indoor environments, and found that mifly consistently localized the drone to within fewer than 7 cm. “As our understanding of perception and computing improves, we often forget about signals that are beyond the visible spectrum. Here, we’ve looked beyond GPS and computer vision to millimeter waves, and by doing so, we’ve opened up new capabilities for drones in indoor environments that were not possible before,” says researcher Fadel Adib, associate professor in the Department of Electrical Engineering and Computer Science, director of the Signal Kinetics group in the MIT Media Lab. The team set out to create a system that could work with just one tag, so it would be cheaper and easier to implement in commercial environments. To ensure the device remained low power, they designed a backscatter tag that reflects millimeter wave signals sent by the onboard radar. Using modulation, the team configured the tag to add a small frequency to the signal it scatters back to the UAV. “Now, the reflections from the surrounding environment come back at one frequency, but the reflections from the tag come back at a different frequency. This allows us to separate the responses and just look at the response from the tag,” says Laura Dodds, research assistant. However, with just one tag and one radar, the researchers could only calculate distance measurements and needed multiple signals to compute the location. Instead of more tags, they added a second radar to the UAV, mounting one horizontally and one vertically with horizontal and vertical polarization and different modulation frequencies. Adding polarization into the tag’s antennas isolate the reflection from each radar. This dual-polarization and dualmodulation architecture gives the spatial location. “The UAV rotation adds a lot of ambiguity to the millimeter wave estimates. This is a big problem because UAVs rotate quite a bit as they are flying,” says Dodds. This is overcome by using the data from the onboard inertial measurement unit (IMU) and fusing the data with the millimetre wave signals. As well as the 7 cm accuracy in dark environments, the system was nearly as accurate in situations where the tag was blocked from view, with reliable localization estimates up to 6 m from the tag. Radar Two mmWave radars are used with a single reflective tag for self-localization (Image courtesy of MIT) Millimetre wave radar for UAV localisation
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