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MAIN RESEARCH AREAS:

Sensor Fusion

Combining sensory data from4diverse and partially redundant sources could lead to a fused solution that is difficult to measure directly and/or has better quality (in terms of accuracy and/or reliability) than the output of each contributing sensor. Additionally, having a global view of information from multiple sources could also reveal the strength and limitation of individual sensors. The sensor fusion research at FCSL is currently focused on three main areas:

  1. A systematic comparison of different nonlinear filtering methods. Within this effort, different formulations of Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Extended Information Filter (EIF), Unscented Information Filter (UIF), and Particle Filter (PF) are compared in terms of estimation performance and computational cost. A total of 23 sets of flight data selected from more than 100 flight test experiments are used in this comparison study to represent a wide range of flight scenarios - including on-board instrumentation and configuration, weather conditions, and mission profiles.
  2. Multiple sensor fusion and fault tolerance. This effort involves the development of a scalable multiple sensor fusion algorithm, off-line and on-line calibration of individual sensors, and the design of sensor failure detection and accommodation algorithms.
  3. Stability analysis for nonlinear filtering methods. A Lyapunov stability analysis is being performed for extended kalman filter and unscented kalman filter with respect to a specific formulation of GPS/INS sensor fusion.

Current applications of the sensor fusion research include improved GPS/INS sensor fusion performance, vision-based navigation, navigation in a GNSS-denied environment, aircraft sensor failure detection and accommodation, low-cost geo-referencing, aircraft structure modeling, and wind gust estimation.

Recent Publications

Gross, J., Gu, Y., Rhudy, M., Gururajan, S., and Napolitano, M.R., “Flight Test Evaluation of Sensor Fusion Algorithms for Attitude Estimation,” IEEE Transactions on Aerospace and Electronic Systems, In Press, June, 2011.

Rhudy, M., Gu, Y., Gross, J., and Napolitano, M. R., “Sensitivity Analysis of EKF and UKF in GPS/INS Sensor Fusion,” 2011 AIAA Guidance, Navigation, and Control Conference, Portland, OR, August, 2011.

Gross, J., Gu, Y., Rhudy, M., Barchesky, F., and Napolitano, M.R. “On-line Modeling and Calibration of Low-Cost Navigation Sensors,” 2011 AIAA Modeling and Simulation Technologies Conference, Portland, OR, August, 2011.

Barchesky, F.,Gross, J., Gu, Y., Rhudy, M., Gururajan, S., and Napolitano, M.R. “Development of a GPS/INS Sensor Fusion Simulation Environment Using Flight Data,” 2011 AIAA Modeling and Simulation Technologies Conference, Portland, OR, August, 2011.