TL;DR:
- NASA collaborates with Mosaic ATM to develop deep learning-based signal strength prediction for AAM aircraft at low altitudes.
- Mosaic ATM uses high-fidelity simulated and real-world data, combining convolutional neural networks and deep learning to achieve highly accurate results.
- The system is approximately ten times faster than current physics-based algorithms, enhancing low-altitude Urban Air Mobility (UAM) communications.
- The goal is to provide a four-minute estimate, factoring in atmospheric effects, transmitter power, Doppler shifts, multipath interference, terrain obstructions, and more.
- Applications include pre-flight planning, route contingency management, transmitter health assessment, and transmitter location optimization.
- Dr. Frederick Wieland leads the project at Mosaic ATM, known for its long-standing collaboration with NASA.
Main AI News:
In the pursuit of advancing air mobility (AAM) technologies, the National Aeronautics and Space Administration (NASA) has sought a pioneering solution. Their mission is to harness the power of deep learning for predicting signal strength at low altitudes for AAM aircraft. What is the agency’s choice for this ambitious endeavor? Mosaic ATM, based in Leesburg, Virginia.
Mosaic ATM is leveraging a machine learning system that undergoes rigorous training using a blend of high-fidelity physics-based simulated data and real-world data collected from test flights. Employing a fusion of convolutional neural networks and deep learning methodologies, this system boasts remarkable precision in signal strength estimation. It achieves this with a velocity that outpaces current physics-based algorithms by a factor of ten, and its primary application lies in enhancing low-altitude Urban Air Mobility (UAM) communication systems.
At the core of Mosaic ATM’s mission is the goal to swiftly deliver a dependable signal strength estimate for advanced air mobility vehicles, all within a remarkable four-minute timeframe. This estimation accounts for a myriad of factors, including atmospheric variables, transmitter power, Doppler shifts, multipath interference, terrain obstructions, and more. This toolkit represents a prime example of how machine learning systems can be trained to grasp the intricacies of physics, particularly signal propagation. The potential applications are extensive, ranging from pre-flight planning and route contingency management to transmitter health status assessment and optimal transmitter location placement, among others.
Leading this groundbreaking project is Dr. Frederick Wieland, the Chief Research Scientist at Mosaic ATM. The collaboration between Mosaic ATM and NASA dates back to the early days of Mosaic’s inception in 2004, through various Small Business Innovative Research (SBIR) contracts and NASA Research Announcement (NRA) contracts. This long-standing partnership underscores the trust NASA places in Mosaic ATM’s innovative prowess, as they continue to shape the future of AAM technologies.
Conclusion:
NASA’s partnership with Mosaic ATM to develop a cutting-edge signal strength prediction toolkit for advanced air mobility (AAM) represents a significant leap forward in the AAM market. The incorporation of deep learning and real-world data into this technology promises not only enhanced safety and efficiency for AAM vehicles but also opens doors for broader applications in urban air transportation. This collaboration underscores the industry’s commitment to innovation and the potential for AAM to revolutionize the way we travel in the future.
