Evaluation of a novel attitude estimation algorithm for a high performance fighter aircraft

This paper discusses the evaluation of a novel attitude estimation algorithm (Kamali et al. (2013)) for a high performance fighter aircraft. This algorithm employs a new modelling approach in the Extended Kalman Filtering (EKF) framework to estimate aircraft attitude information without using forward acceleration sensor, Global Positioning System (GPS) sensors or magnetometer. Evaluation of the algorithm is conducted using flight data from a high performance fighter aircraft and using flight simulation data. Estimation results during various manoeuvres such as full rolls, inverted loops, split-S manoeuvres, steep climbs, and dives, are studied. Effect of different wind perturbations (gust, shear, turbulence, etc.), on estimation results, is also studied. Results using flight data are compared with those obtained from a GPS-assisted Inertial Navigation System (INS), whereas for results using simulation data, the simulation itself provides true values. Conclusions are drawn for the performance of the algorithm based on simulation and flight data.