Engineers have developed a method to identify objects using microwaves that improves accuracy while reducing the associated computing time and power requirements. The system could provide a boost to object identification and speed in fields where both are critical such as autonomous vehicles, security screening, and motion sensing.
The machine-learning approach skips the step of creating an image for analysis by a human and instead analyzes the pure data directly. It also jointly determines optimal hardware settings that reveal the most important data while simultaneously discovering what the most important data actually is. In a proof-of-principle study, the setup correctly identified a set of 3D numbers using tens of measurements instead of the hundreds or thousands typically required.
Researchers use a metamaterial antenna that can sculpt a microwave wave front into many different shapes. In this case, the metamaterial is an 8 × 8 grid of squares, each of which contains electronic structures that allow it to be dynamically tuned to either block or transmit microwaves.
For each measurement, the intelligent sensor selects a handful of squares to let microwaves pass through. This creates a unique microwave pattern that bounces off the object to be recognized and returns to another similar metamaterial antenna. The sensing antenna also uses a pattern of active squares to add further options to shape the reflected waves. The computer then analyzes the incoming signal and attempts to identify the object.
By repeating this process thousands of times for different variations, the machine learning algorithm eventually discovers which pieces of information are the most important as well as which settings on both the sending and receiving antennas are the best at gathering them. The transmitter and receiver act together and are designed together by the machine learning algorithm. This co-design of measurement and processing allows all the a priori knowledge about the task, scene, and measurement constraints to be used to optimize the entire sensing process.
After training, the machine learning algorithm landed on a small group of settings, cutting down on the number of measurements, time, and computational power it needs. Instead of the hundreds or even thousands of measurements typically required by traditional microwave imaging systems, it could see the object in less than 10 measurements.
Whether or not this level of improvement would scale up to more complicated sensing applications is still in question. But the researchers are already trying to use the new concept to optimize hand motion and gesture recognition for next-generation computer interfaces. There are other domains where improvements in microwave sensing are needed and the small size, low cost, and easy manufacturability of these types of metamaterials make them promising candidates for future devices. Microwaves are ideal for applications like concealed threat detection, identifying objects on the road for driverless cars, or monitoring for emergencies in assisted-living facilities.
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