We have been working for some time on a way to estimate and display important flight parameters such as angle of attack, estimated airspeed (on UAVs that do not have Pitot tubes), side slip angle or flight path vector (FPV) using a simple, robust dynamical model of the UAV and a computational method that is both fast and has computational time guarantees.

Using a hybrid systems approach, the problem boils down to finding a set of analytical solutions to least squares problems, and to identify which solution is the most likely based on an analysis of the residuals. The method executes in real time, at 50 Hz, on an APM. We tested it using hardware-in-the-loop simulation (with X-Plane). As you can see from the video, the real and estimated airspeed closely match (we added some noise to the X-Plane generated IMU data to simulate the actual performance of the APM's IMU), and the FPV/AOA/sideslip evolve realistically.

Because the dynamical model used in this algorithm is fairly robust, it requires only a small number of parameters to work. More details on our WIP paper at RTAS 2014: https://www.mpi-sws.org/~bbb/proceedings/rtas14-wip-proceedings.pdf

The Distributed Sensing Systems Laboratory (DSS) is a research group with the systems subgroup of the Electrical Engineering department of King Abdullah University of Science and Technology (KAUST). Among our research activities, we are interested in the development of Lagrangian-based flash-flood sensing systems (and the associated control/estimation algorithms). We are using C-17 remote controlled airplanes for testing, and these airplanes are controlled by APMs 2.6 (with a few modifications to the original code) and Gumstix COMs:

The sensing concept is related in http://www.newscientist.com/article/mg21829185.500-swarm-of-drones-to-give-early-warning-of-flash-floods.html

We are currently 3D printing the sensors. Stay tuned for the future sensor drop tests!