Attitude and heading refernce system I-AHRS for the EFIGENIA autonomous unmanned aerial vehicles UAV based on MEMS sensor and a neural network strategy for attitude estimation

For the autonomous flight, navigation, guidance and control of the EFIGENIA unmanned aerial vehicle it is essential to have high performance 6-DOF attitude and heading reference system measurements. This paper presents the design and development of a real-time intelligent attitude and heading reference system I-AHRS, as in the hardware, as in the intelligent digital neural network software scheme, analysis, design and construction for the orientation calculation for the EFIGENIA EJ-1B MOZART and the EFIGENIA EJ-2B MARIA autonomous unmanned aerial vehicles UAVs. The EFIGENIA I-AHRS consists of three MEMS accelerometers, three MEMS rate-gyros and three magneto-resistive transducers that send its outputs to a digital neural network in which is possible to develop a strategy for attitude estimation. Additionally it is well known that the Kalman Filter is an option as multi-sensor data fusion and integration, however it has some adaptability limitations. In this paper, FPGA reconfigurable hardware digital neural network architecture is presented and utilized to replace the Kalman Filter in the integration of MEMS IMU inertial sensors signals and the Magneto resistive sensors.