Implementing Compressed Sensing in Applied Projects

We are contemplating using Compressed Sensing in three different projects:

• The Hyper-GeoCam project: This is a payload that will be flown on the HASP platform in September. Last year, we flew a simple camera that eventually produced a 105 km panorama of New Mexico. We reapplied for the same program and have been given the OK for two payloads. The same GeoCam will be re-flown so that we can produce a breath taking panorama from 36 km altitude. The second payload is essentially supposed to be a hyperspectral imager on the cheap: i.e. a camera and some diffraction gratings allowing a fine decomposition of the reflected sun light from the ground. The project is called Hyper-GeoCam and I expect to implement a random lens imager such as the one produced at MIT. Tests will be performed on the SOLAR platform.
  
• The DARPA Urban Challenge: We have a car selected in the track B: We do not have Lidars and need to find ways to navigate in an urban settings with little GPS availability. The autonomous car is supposed to be navigating in a mock town and follow the rules of the California traffic laws, that includes passing other cars.
  
• Solving the Linear Boltzmann equation using compressed sensing techniques:The idea is that this equation has a known suite of eigenfunctions (called Case eigenfunctions) and because they are very difficult to use and expand from, it might be worth a try to look into the compressed sensing approach to see if it solves the problem more efficiently.