Engineers have shown that using a type of AI that revolutionized computer vision can greatly enhance modern adaptive radar systems. And in a move that parallels the impetus of the computer vision boom, they have released a large dataset of digital landscapes for others to build on their work.
AI approaches and an enormous open-source dataset could spark rapid advancements in adaptive radar systems similar to those seen in computer vision over the past two decades.
"Classical radar methods are very good, but they aren't good enough to meet industry demands for products such as autonomous vehicles," said Shyam Venkatasubramanian, a graduate research assistant working in the lab of Vahid Tarokh, the Rhodes Family Professor of Electrical and Computer Engineering at Duke."We're working to bring AI into the adaptive radar space to tackle problems like object detection, localization and tracking that industry needs solved.
To move adaptive radar into the age of AI, Venkatasubramanian and Tarokh were inspired by the history of computer vision. In 2010, researchers at Stanford University released an enormous image database consisting of over 14 million annotated images called ImageNet. Researchers around the world used ImageNet to test and compare new AI approaches that became industry standard.
While the group has yet to test their methods in the field, they benchmarked their AI's performance on a modeling and simulation tool called RFView®, which gains its accuracy by incorporating the Earth's topography and terrain into its modeling toolbox. Then, continuing in the footsteps of computer vision, they created 100 airborne radar scenarios based on landscapes from across the contiguous United States and released it as an open-source asset called"RASPNet.
The scenarios included were handpicked by radar and machine learning experts and have a wide range of geographical complexity. On the easiest side for adaptive radar systems to handle is the Bonneville Salt Flats, while the hardest is Mount Rainier. Venkatasubramanian and his group hope that others will take their ideas and dataset and build even better AI approaches.
Shyam Venkatasubramanian, Sandeep Gogineni, Bosung Kang, Ali Pezeshki, Muralidhar Rangaswamy, Vahid Tarokh.Artificial intelligence combined with a novel bio-inspired camera achieves 100 times faster detection of pedestrians and obstacles than current automotive cameras. This important step for ...
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