A Neural Network Application for Non-Gyroscope Based Aircraft Attitude Determination

Aircraft accurate navigation and control depends on the estimation framework concerning attitude and position. Aircraft rotation can be obtained, by measuring two different vectors, for instance, gravity and velocity, in two distinct reference coordinated systems: body and inertial local horizon axes. The inertial reference frame's velocity vector is determined using Global Navigation Satellite Systems sensors, while the body reference frame's velocity vector is obtained through integrating accelerometer measurements. To estimate gravity vector in the body-fixed reference frame, an approach is introduced, based on a neural network that determines aircraft aerodynamic forces. Through the derivation of flight dynamics equations, the vector of gravity is finally determined. The combination of these vectors facilitates the body rotation determination. Simulations, employing nonlinear flight dynamics models, demonstrate that accurate aircraft attitude determination is attainable through the integration of just accelerometers, Global Navigation Satellite Systems sensors, and the proposed methodology.