Validation Tools for an Information Fusion Based Integrated Flight Safety Monitor

Sponsor: NASA Langley Research Center, NASA Aviation Safety Program (AvSP), Vehicle Systems Safety Technologies (VSST) Project.

Information fusion algorithms, including nonlinear stochastic estimators, provide important tools for the development of aircraft vehicle health management, flight safety assurance, and resilient guidance & control sub-systems. Despite its growing popularity, research in the validation of nonlinear stochastic estimators is very limited. Current results are often based on highly restrictive assumptions that are of limited practical value. The first objective of the research project is therefore to develop analytical validation tools for several classes of nonlinear stochastic filtering algorithms. This includes the evaluation of stability, coverage, convergence rate, and accuracy of the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Extended Information Filter (EIF), and Unscented Information Filter (UIF) under realistic uncertainty assumptions.

The second objective of the project is to develop information fusion based tools for real-time monitoring and prediction of the Pilot Vehicle System (PVS) closed-loop stability. Modern aircraft systems have a highly complex closed-loop starting from sensors, signal processing, displaying and cueing, human pilot, flight control, signal distribution, to actuators, which finally control the aircraft. Current aviation safety research has emphasized the modeling aspect of individual sub-systems within this link. However, a time-varying pilot control model is needed for deriving the closed-loop PVS describing function. This would in turn help us to evaluate PVS stability margins, to predict unsafe flight conditions, and to develop mitigation approaches. Additionally, an improved understanding of low-level human control strategies will help us to better evaluate the impact of new technologies, such as adaptive sensing and control algorithms, on the safety of future aircraft systems.

Press Release: WVU's Gu to study safety of new-generation aircraft

Publications

1. Gu, Y., Gross, J., Rhudy, M., Lassak, K, “A Fault-Tolerant Multiple Sensor Fusion Approach Applied to UAV Attitude Estimation,” In Press, International Journal of Aerospace Engineering, 2016.
2. Mandal, T., Gu, Y., "Online Pilot Model Parameter Estimation Using Sub-Scale Aircraft Flight Data," Invited, AIAA SciTech Conference, San Diego, CA, Jan 2016.
3. Lassak, K., Gu, Y., "Real-Time Extended Kalman Filter Stability Indicator," Invited, AIAA SciTech Conference, San Diego, CA, Jan 2016.
4. Rhudy, M., Gu, Y., Chao, H., and Gross, J., "Unmanned Aerial Vehicle Navigation Using Wide-Field Optical Flow and Inertial Sensors," Journal of Robotics, Volume 2015, Article ID 251379, Oct 2015.
5. Rhudy, M., Chao, H., Gu, Y., "Wide-Field Optical Flow Aided Inertial Navigation for Unmanned Aerial Vehicles," 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Chicago, September, 2014.
6. Mandal, T., and Gu, Y., "Pilot-Vehicle System Modeling Using Sub-Scale Flight Experiments," AIAA Guidance, Navigation, and Control Conference, Washington DC, January 2014.
7. Rhudy, M., Gu, Y., and Napolitano, M.R., "Relaxation of Stability Requirements for Extended Kalman Filter Stability within GPS/INS Attitude Estimation," AIAA Guidance, Navigation, and Control Conference, Washington DC, January 2014
8. Rhudy, M., Gu, Y., "Online Stochastic Convergence Analysis of the Kalman Filter," International Journal of Stochastic Analysis, vol. 2013, Article ID 240295, 2013. doi:10.1155/2013/240295.
9. Rhudy, M., Gu, Y., and Napolitano, M.R., "Low-Cost Loosely-Coupled Dual GPS/INS for Attitude Estimation with Application to a Small UAV," AIAA Guidance Navigation and Control Conference, Boston, MA, August 2013.
10. Rhudy, M., Gu, Y., and Napolitano, M.R., "Does the Unscented Kalman Filter Converge Faster than the Extended Kalman Filter?  A Counter Example," AIAA Guidance Navigation and Control Conference, Boston, MA, August 2013.
11. Lassak, K., Rhudy, M., and Gu, Y., "On-Line Orientation Calibration of Inertial Measurement Unit Pairs using Unmanned Aerial Vehicle Flight Data," AIAA Guidance Navigation and Control Conference, Boston, MA, August 2013.
12. Mandal, T., Gu, Y., Chao, H., and Rhudy, M., "Flight Data Analysis of Pilot-Induced-Oscillations of a Remotely Piloted Aircraft," AIAA Guidance Navigation and Control Conference, Boston, MA, August 2013.