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

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4 Author(s)
Carminati, M. ; Dipartimento di Elettronica e Informazione, Politecnico di Milano, Milan, Italy ; Ferrari, G. ; Grassetti, R. ; Sampietro, M.

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.

Published in:

Sensors Journal, IEEE  (Volume:12 ,  Issue: 10 )

Date of Publication:

Oct. 2012

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