Accurate Attitude Estimation Based on Adaptive UKF and RBF ANN Network
Using Fusion Methodology for Low Cost Micro IMU Applied to Rotating
Environment
Abstract
An Adaptive Unscented Kalman Filter (AUKF) method combining sensor
fusion algorithm with Artificial Neural Network (ANN) is designed for
high precision attitude tracking of low cost, small size
Micro-Electro-Mechanical System (MEMS) Inertial Measurement Unit (IMU)
in high dynamic environment. The different control strategies fusing
multi MEMS inertial sensors are adopted under various dynamic
situations. The AUKF attitude estimation approach utilizing the MEMS
sensor and Global Positioning System (GPS) could provide reliable
estimation in high dynamic environmental variations. The adaptive scale
factor is used to adaptively weaken or enhance the effects on new
measurement data through the adjustment of the estimation according to
the predicted residual vector. To solve the problem that new measurement
data could not be updated in case of GPS failure situation, an attitude
algorithm based on RBF-ANN feedback correction is proposed to apply in
AUKF. The estimated deviation of predicted non-augmented system state
would be provided based on Radial Basis Function (RBF)-ANN. The
corrected the predicted non-augmented system state would be used for
estimation process in AUKF. The experiment platform simulating the
rotation of spinning projectile the was setup. The comparative
experimental results show better control performance of the proposed
method under various dynamic conditions.