Multiconstellation satellite navigation is critical in signal-degraded environments where signals are strongly corrupted. In this case, the use of a single GNSS system does not guarantee an accurate and continuous positioning. A possible approach to solve this problem is the use of multiconstellation receivers that provide additional measurements and allows robust reliability testing; in this work, a GPS/GLONASS combination is considered. In urban scenario, a modification of the classical RAIM technique is necessary taking into account frequent multiple blunders. The FDE schemes analysed are the “Observation Subset Testing,” “Forward-Backward Method,” and “Danish Method”; they are obtained by combining different basic statistical tests. The considered FDE methods are modified to optimize their behaviour in urban scenario. Specifically a preliminary check is implemented to screen out bad geometries. Moreover, a large blunder could cause multiple test failures; hence, a separability index is implemented to avoid the incorrect exclusion of blunder-free measurements. Testing the RAIM algorithms of GPS/GLONASS combination to verify the benefits relative to GPS only case is a main target of this work too. The performance of these methods is compared in terms of RMS and maximum error for the horizontal and vertical components of position and velocity.
Although very uncommon, the sequential failures of all aircraft Pitot tubes, with the consequent loss of signals for all the dynamic parameters from the Air Data System, have been found to be the cause of a number of catastrophic accidents in aviation history. This paper proposes a robust data-driven method to detect faulty measurements of aircraft airspeed, angle of attack, and angle of sideslip. This approach first consists in the appropriate selection of suitable sets of model regressors to be used as inputs of neural network-based estimators to be used online for failure detection. The setup of the proposed fault detection method is based on the statistical analysis of the residual signals in fault-free conditions, which, in turn, allows the tuning of a pair of floating limiter detectors that act as time-varying fault detection thresholds with the objective of reducing both the false alarm rate and the detection delay. The proposed approach has been validated using real flight data by injecting artificial ramp and hard failures on the above sensors. The results confirm the capabilities of the proposed scheme showing accurate detection with a desirable low level of false alarm when compared with an equivalent scheme with conventional “a priori set” fixed detection thresholds. The achieved performance improvement consists mainly in a substantial reduction of the detection time while keeping desirable low false alarm rates.
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