Real-Time Data Fusion and MEMS Sensors Fault Detection in an Aircraft Emergency Attitude Unit Based on Kalman Filtering

The design, realization, and experimental validation of an original avionic attitude estimation unit are presented. The core of the system is a nine-state extended Kalman filter that optimally blends complementary kinematic data provided by orthogonal triads of inertial micro-electro-mechanical systems sensors: rate gyros (short-term fast dynamics) and accelerometers (long-term static reference). The unit is embedded in a novel aircraft emergency guidance system based on miniaturized solid-state sensors. While achieving the required extreme compactness, state-of-the-art performance is preserved: 50 Hz update rate, 0.1
$^{\circ}$
angular resolution, 0.5
$^{\circ}$
static accuracy, and 2
$^{\circ}$
dynamic accuracy (400
$^{\circ}/{\rm s}$
max. angular rate, 10 g max. acceleration), all experimentally verified and granted over the extended thermal range. The selection of the state variables has been carefully trimmed in order to maximize the performance/speed tradeoff for real-time running in an embedded processor. The adoption of the Kalman observer also enables the implementation of model-based sensor fault detection with no extra computational cost.