Fault-tolerant sensor fusion for aircraft height estimation

[摘要] ENGLISH ABSTRACT: This thesis presents the design and verification of a fault-tolerant sensor fusion system toprovide robust height estimates for commercial airliners. Current commercial aircraft systems obtain a height estimate by taking the median of redundant radio altimeter measurements.However, this approach is not robust to a failure that affects all radio altimeters.The proposed system uses technology redundancy by combining the measurements from the available aircraft sensors that provide either height or altitude measurements: the radio altimeter, the GPS, the inertial sensors, and the instrument landing system (ILS).An onboard terrain map is used to convert altitude measurements to height measurements.The robust height estimation problem is divided into two major sub-problems: fault detection and isolation and sensor fusion. To address the fault detection and isolationsub-problem, a variety of data-driven and model-based techniques were investigated. Twogeneral approaches were considered: fault diagnosis using sensor measurements from asingle time instant, and fault diagnosis using sensor measurements from a window of consecutivetime instants. For the single time instant approach, only data-driven techniques were applied, including outlier detectors, binary classifiers, and multi-class classifiers. Forthe multiple time instant approach, both model-based and data-driven techniques wereapplied. The model-based techniques included the Bank of Kalman filters and the RobustKalman filter, while the data-driven techniques include Model Consensus, Dynamic Principal Component Analysis, and Gaussian Naïve Bayes with time as an additionalvariable. Finally, to address the sensor fusion sub-problem, three methods were investigated:using the median of the sensor measurements, using the weighted average of the sensor measurements, and using a Kalman filter to perform optimal sensor fusion.A simulation model was used to generate synthetic training and testing data for thedata-driven techniques. Mathematical models were established for the aircraft motion,the sensors, and the terrain. The structure and nominal parameters for the sensor models were based on information sourced from literature, and then the sensor parameters were tuned to fit a real dataset provided by Airbus. Fault models for six types of sensor faults were also created. The simulation model was used to generate a large dataset of representative sensor measurements containing both \no-fault and \fault conditions.The fault detection and isolation, and the sensor fusion were tested using both simulated data and real datasets of actual ight data with synthetic sensor failures injected. Thesingle time instant approach achieved fault diagnosis accuracies from 85% (Support VectorMachine) to 94% (k-Nearest Neighbors). The multiple time instant approach achievedfault diagnosis accuracies from 93% (Model Consensus) to 99% (Robust Kalman filter).The three sensor fusion approaches produced height estimates with average accuracies from 5:1m to 7:4 m.

[发布机构] Stellenbosch University