Analysis of Model-Aided Navigation of Unmanned Aerial Vehicles

To overcome the rapid and unbounded error growth of low-cost Inertial Navigation Systems (INS), aircraft localization methods commonly compensate for Inertial Measurement Unit (IMU) sensor errors by integrating them with Global Positioning System (GPS) measurements via a Kalman Filter. However, over the past decade, the potential of GPS jamming or even spoofing GPS signals has forced the research community to focus on the development of GPS-denied navigation technologies. Among the GPS-denied techniques, one approach that has been considered is the use of a Vehicle Dynamic Models (VDM) to reduce the rate at which an INS becomes unusable. As such, this Master\u27s thesis considers the use of different aerodynamic modeling approaches to aid in compensation of IMU errors of a fixed-wing Unmanned Aerial Vehicle (UAV). The goals of this research are to evaluate the sensitivity of the performance of dynamic model aided navigation in the context of low-cost platforms where performance benefit must be weighed against the complexity that is required to develop the dynamic model. To do this, first, in simulation, the sensitivity to the required modeling accuracy is shown by perturbing the model coefficients with errors. In addition, different sensors and sensor grades are evaluated, and three different model-aided navigation architectures are discussed and evaluated. To conduct this work, a UAV simulation is developed within which a UAV trajectory is driven by ``truth\u27\u27 dynamic model and then IMU measurements are derived and errors are added to them using standard stochastic models for IMU sensors. Finally, the algorithm performance is then evaluated using actual UAV flight testing data from a low cost testbed equipped with GPS and IMU sensors. The testbed used and modeled is a 2.4 m span fixed wing UAV designed and instrumented at WVU

Flight-Test Evaluation of Kinematic Precise Point Positioning of Small UAVs

An experimental analysis of Global Positioning System (GPS) flight data collected onboard a Small Unmanned Aerial Vehicle (SUAV) is conducted in order to demonstrate that postprocessed kinematic Precise Point Positioning (PPP) solutions with precisions approximately 6 cm 3D Residual Sum of Squares (RSOS) can be obtained on SUAVs that have short duration flights with limited observational periods (i.e., only ∼≤5 minutes of data). This is a significant result for the UAV flight testing community because an important and relevant benefit of the PPP technique over traditional Differential GPS (DGPS) techniques, such as Real-Time Kinematic (RTK), is that there is no requirement for maintaining a short baseline separation to a differential GNSS reference station. Because SUAVs are an attractive platform for applications such as aerial surveying, precision agriculture, and remote sensing, this paper offers an experimental evaluation of kinematic PPP estimation strategies using SUAV platform data. In particular, an analysis is presented in which the position solutions that are obtained from postprocessing recorded UAV flight data with various PPP software and strategies are compared to solutions that were obtained using traditional double-differenced ambiguity fixed carrier-phase Differential GPS (CP-DGPS). This offers valuable insight to assist designers of SUAV navigation systems whose applications require precise positionin

Interval Fuzzy Model for Robust Aircraft IMU Sensors Fault Detection

This paper proposes a data-based approach for a robust fault detection (FD) of the inertial measurement unit (IMU) sensors of an aircraft. Fuzzy interval models (FIMs) have been introduced for coping with the significant modeling uncertainties caused by poorly modeled aerodynamics. The proposed FIMs are used to compute robust prediction intervals for the measurements provided by the IMU sensors. Specifically, a nonlinear neural network (NN) model is used as central prediction of the sensor response while the uncertainty around the central estimation is captured by the FIM model. The uncertainty has been also modelled using a conventional linear Interval Model (IM) approach; this allows a quantitative evaluation of the benefits provided by the FIM approach. The identification of the IMs and of the FIMs was formalized as a linear matrix inequality (LMI) optimization problem using as cost function the (mean) amplitude of the prediction interval and as optimization variables the parameters defining the amplitudes of the intervals of the IMs and FIMs. Based on the identified models, FD validation tests have been successfully conducted using actual flight data of a P92 Tecnam aircraft by artificially injecting additive fault signals on the fault free IMU readings

Air Data Sensor Fault Detection with an Augmented Floating Limiter

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