Kernel-based fault diagnosis of inertial sensors using analytical redundancy

Kernel methods are able to exploit high-dimensional spaces for representational advantage, while only operating implicitly in such spaces, thus incurring none of the computational cost of doing so. They appear to have the potential to advance the state of the art in control and signal processing applications and are increasingly seeing adoption across these domains. Applications of kernel methods to fault detection and isolation (FDI) have been reported, but few in aerospace research, though they offer a promising way to perform or enhance fault detection. It is mostly in process monitoring, in the chemical processing industry for example, that these techniques have found broader application. This research work explores the use of kernel-based solutions in model-based fault diagnosis for aerospace systems. Specifically, it investigates the application of these techniques to the detection and isolation of IMU/INS sensor faults – a canonical open problem in the aerospace field. Kernel PCA, a kernelised non-linear extension of the well-known principal component analysis (PCA) algorithm, is implemented to tackle IMU fault monitoring. An isolation scheme is extrapolated based on the strong duality known to exist between probably the most widely practiced method of FDI in the aerospace domain – the parity space technique – and linear principal component analysis. The algorithm, termed partial kernel PCA, benefits from the isolation properties of the parity space method as well as the non-linear approximation ability of kernel PCA. Further, a number of unscented non-linear filters for FDI are implemented, equipped with data-driven transition models based on Gaussian processes - a non-parametric Bayesian kernel method. A distributed estimation architecture is proposed, which besides fault diagnosis can contemporaneously perform sensor fusion. It also allows for decoupling faulty sensors from the navigation solution

Unscented Kalman Filter for State and Parameter Estimation in Vehicle Dynamics

Automotive research and development passed through a vast evolution during past decades. Many passive and active driver assistance systems were developed, increasing the passengers’ safety and comfort. This ongoing process is a main focus in current research and offers great potential for further systems, especially focusing on the task of autonomous and cooperative driving in the future. For that reason, information about the current stability in terms of dynamic behavior and vehicle environment are necessary for the systems to perform properly. Thus, model-based online state and parameter estimation have become important throughout the last years using a detailed vehicle model and standard sensors, gathering this information. In this chapter, state and parameter estimation in vehicle dynamics utilizing the unscented Kalman filter is presented. The estimation runs in real time based on a detailed vehicle model and standard measurements taken within the car. The results are validated using a Volkswagen Golf GTE Plug-In Hybrid for various dynamic test maneuvers and a Genesys Automotive Dynamic Motion Analyzer (ADMA) measurement unit for high-precision measurements of the vehicle’s states. Online parameter estimation is shown for friction coefficient estimation performing maneuvers on different road surfaces

Kalman and particle filtering methods for full vehicle and tyre identification

This paper considers identification of all significant vehicle handling dynamics of a test vehicle, including identification of a combined-slip tyre model, using only those sensors currently available on most vehicle controller area network buses. Using an appropriately simple but efficient model structure, all of the independent parameters are found from test vehicle data, with the resulting model accuracy demonstrated on independent validation data. The paper extends previous work on augmented Kalman Filter state estimators to concentrate wholly on parameter identification. It also serves as a review of three alternative filtering methods; identifying forms of the unscented Kalman filter, extended Kalman filter and particle filter are proposed and compared for effectiveness, complexity and computational efficiency. All three filters are suited to applications of system identification and the Kalman Filters can also operate in real-time in on-line model predictive controllers or estimators

Vehicle sideslip angle measurement based on sensor data fusion using an integrated ANFIS and an Unscented Kalman Filter algorithm

Most existing ESC (Electronic Stability Control) systems rely on the measurement of both yaw rate and sideslip angle. However, one of the main issues is that the sideslip angle cannot be measured directly because the sensors are too expensive. For this reason, sideslip angle estimation has been widely discussed in the relevant literature. The modeling of sideslip angle is complex due to the non-linear dynamics of the vehicle. In this paper, we propose a novel observer based on ANFIS, combined with Kalman Filters in order to estimate the sideslip angle, which in turn is used to control the vehicle dynamics and improve its behavior. For this reason, low-cost sensor measurements which are integrated into the actual vehicle and executed in real time have to be used. The ANFIS system estimates a "pseudo-sideslip angle" through parameters which are easily measured, using sensors equipped in actual vehicles (inertial sensors and steering wheel sensors); this value is introduced in UKF in order to filter noise and to minimize the variance of the estimation mean square error. The estimator has been validated by comparing the observed proposal with the values provided by the CARSIM model, which is a piece of experimentally validated software. The advantage of this estimation is the modeling of the non-linear dynamics of the vehicle, by means of signals which are directly measured from vehicle sensors. The results show the effectiveness of the proposed ANFIS+UKF-based sideslip angle estimator

Integrated solution for anomalous driving detection based on BeiDou/GPS/IMU measurements

© 2016 Elsevier Ltd.There has been an increasing role played by Global Navigation Satellite Systems (GNSS) in Intelligent Transportation System (ITS) applications in recent decades. In particular, centimeter/decimetre positioning accuracy is required for some safety related applications, such as lane control, collision avoidance, and intelligent speed assistance. Lane-level Anomalous driving detection underpins these safety-related ITS applications. The two major issues associated with such detection are (1) accessing high accuracy vehicle positioning and dynamic parameters; and (2) extraction of irregular driving patterns from such information. This paper introduces a new integrated framework for detecting lane-level anomalous driving, by combining Global Positioning Systems (GPS), BeiDou, and Inertial Measurement Unit (IMU) with advanced algorithms. Specifically, we use Unscented Particle Filter (UPF) to perform data fusion with different positioning sources. The detection of different types of Anomalous driving is achieved based on the application of a Fuzzy Inference System (FIS) with a newly introduced velocity-based indicator. The framework proposed in this paper yield significantly improved accuracy in terms of positioning and Anomalous driving detection compared to state-of-the-art, while offering an economically viable solution for performing these tasks

Sensitivity Analyses of Optimized Attitude Estimators Using Sensor Fusion Solutions for Low-Cost MEMS Configurations

Since the 1990’s, there has been increased focus on creating navigation systems for small unmanned systems, particularly small unmanned aerial systems (SUAS). Due to size, weight, and cost restrictions, compared to larger more costly manned systems, navigation systems for SUAS have evolved to be quite different from the proven systems of the past. Today, there are many solutions for the problem of navigation for SUAS. The problem has now become choosing the most fitting navigation solution for a given application/mission. This is particularly true for evaluating solutions that are fundamentally different. This research analyses the performance and sensitivity of four sensor fusion solutions for attitude estimation under multiple simulated flight conditions. There are three different hardware configurations between the four estimators. For this reason, each estimator is tuned to be experimentally optimal, as to provide a fair comparison between different estimators. With each estimator tuned to its highest performance, the estimators are compared based on their sensitivity to tuning error, sensor bias, and estimator initialization error. Finally the estimators\u27 accuracy performances are directly compared. This thesis also provides methods to tune different configuration estimators to their individual best performances. These methods show that choosing tuning parameters based on sensor noise covariance, as is typically done in research, does not produce optimal performance for all estimator formulations. After comparing multiple sensitivity and performance properties of the estimators, observations are provided regarding the efficacy of the analyses, including the applicability of the metrics used to determine performance. Some metrics where shown to be misleading for particular estimators or analyses. Ultimately, guidance is given for choosing performance metrics capable of comparing different solutions

GPS/INS Integration Accuracy Enhancement Using the Interacting Multiple Model Nonlinear Filters

In this paper, performance evaluation for various single model nonlinear filters and nonlinear filters with interactingmultiple model (IMM) framework is carried out. A high gain (high bandwidth) filter is needed to response fast enoughto the platform maneuvers while a low gain filter is necessary to reduce the estimation errors during the uniformmotion periods. Based on a soft-switching framework, the IMM algorithm allows the possibility of using highly dynamicmodels just when required, diminishing unrealistic noise considerations in non-maneuvering situations. The IMMestimator obtains its estimate as a weighted sum of the individual estimates from a number of parallel filters matchedto different motion modes of the platform. The use of an IMM allows exploiting the benefits of high dynamic models inthe problem of vehicle navigation. Simulation and experimental results presented in this paper confirm theeffectiveness of the method

Full vehicle and tyre identification using unscented and extended identifying Kalman Filters

This paper considers identification of all significant vehicle handling and driveline dynamics of a test vehicle, including identification of a combined-slip tyre model, using only those sensors currently available on most vehicle CAN buses. The method extends previous work using augmented Kalman Filter state estimators to concentrate wholly on parameter identification, and it compares Extended and Unscented Kalman filter algorithms. Using an appropriately simple but efficient model structure, all of the independent parameters are found from test vehicle data, with the resulting model accuracy demonstrated on independent validation data. The method is suited to applications of system identification, but also in on-line model predictive controllers or estimators. It can also operate in real-time, so the model could be continuously identified to maintain accuracy with each new journey

A comparison of multiple techniques for the reconstruction of entry, descent, and landing trajectories and atmospheres

The primary importance of trajectory reconstruction is to assess the accuracy of pre-flight predictions of the entry trajectory. While numerous entry systems have flown, often these systems are not adequately instrumented or the flight team not adequately funded to perform the statistical engineering reconstruction required to quantify performance and feed-forward lessons learned into future missions. As such, entry system performance and reliability levels remain unsubstantiated and improvement in aerothermodynamic and flight dynamics modeling remains data poor. The comparison is done in an effort to quantitatively and qualitatively compare Kalman filtering methods of reconstructing trajectories and atmospheric conditions from entry systems flight data. The first Kalman filter used is the extended Kalman filter. Extended Kalman filtering has been used extensively in trajectory reconstruction both for orbiting spacecraft and for planetary probes. The second Kalman filter is the unscented Kalman filter. Additionally, a technique for using collocation to reconstruct trajectories is formulated, and collocation's usefulness for trajectory simulation is demonstrated for entry, descent, and landing trajectories using a method developed here to deterministically find the state variables of the trajectory without nonlinear programming. Such an approach could allow one to utilize the same collocation trajectory design tools for the subsequent reconstruction.Ph.D.Committee Chair: Braun, Robert; Committee Member: Lisano, Michael; Committee Member: Russell, Ryan; Committee Member: Striepe, Scott; Committee Member: Volovoi, Vital