RESILIENT STATE ESTIMATION FOR MICRO AIR VEHICLES UNDER SENSOR ATTACKS

This thesis proposes a solution to the problem of resilient state estimation and sensor fusion in an autonomous micro air vehicle. The setup comprises of redundant sensors that measure the same physical signal. An adversary may spoof a subset of these sensors and send falsified readings to the controller, potentially compromising performance and safety of the system. This work integrates Brooks-Iyengar Sensor fusion algorithm with a generic state estimator as a method to thwart sensor attacks. The algorithm outputs a point estimate and a fusion interval based on an assumed set of faulty sensors. Finally, the thesis illustrates the usefulness of the resilient state estimator with a case study on a MAV flight dataset

INS/GPS Based State Estimation of Micro Air Vehicles Parametric Study Using Extended Kalman Filter (EKF) Schemes

Micro Air Vehicles (MAVs) are classified as the small scale aircrafts which can be remote controlled, semi-autonomous and autonomous. They are highly sensitive to the wind gust and therefore the control of Micro Air Vehicle is a very challenging area. To control an aircraft, the first step is the precise navigation of the MAV and state estimation is the pre requisite. In this paper the states (roll, pitch, yaw, position and wind direction) are estimated with the help of Discrete Time Extended Kalman Filter operating on Inertial Measurement Unit (IMU) which consists of MEMS Gyro, MEMS Accelerometer, MEMS Magnetometer and GPS. These sensors are based on MEMS (Micro Electro Mechanical System) technique which is very helpful for size and weight reduction. Three techniques of Discrete Time Extended Kalman Filter are used named as Single state Discrete Time Extended Kalman, Cascaded Two Stage Discrete Time Extended Kalman Filter and Cascaded Three Stage Discrete Time Extended Kalman Filter. The simulations obtained from these filters are compared and analyzed. Trajectory and sensors data is recorded from Flight Simulator software, and then compared with the simulations obtained from Extended Kalman Filter with the help of MATLAB Software

INS/GPS Based State Estimation of Micro Air Vehicles Using Inertial Sensors

The most attractive and attention seeking topic of aerospace world is Micro Air Vehicle (MAV) which broadly speaking it can be categorized as significantly smaller aircraft than conventional aircrafts. Micro Air Vehicles can be divided as autonomous, semi-autonomous and remote controlled flying machines which can be fixed wing MAV, Flapping wing MAV and rotary wing MAV. One of the crucial problems regarding Micro Air Vehicle is its stability which is basically concerned with the guidance, navigation and control. State Estimation is an important aspect of navigation and this paper deals with the state estimation problem of micro Air vehicle. Inertial sensors are being used including MEMS Gyro, MEMS Accelerometer, Magnetometer and GPS in Inertial Measurement Unit (IMU) and three Discrete Time Extended Kalman Filter Schemes have been used for the state estimation purpose. Trajectory and required data is recorded in Flight Simulator and MATLAB has been used for the simulations. A comprehensive parametric study is carried out and results are analyzed and briefly discussed. Keywords: Micro Electro Mechanical System (MEMS), Micro Air Vehicle (MAV), Measurement Covariance Matrix, Process Covariance Matri

Unmanned Aircraft System Navigation in the Urban Environment: A Systems Analysis

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140665/1/1.I010280.pd

Optimal Control and Estimation Strategies for Nonlinear and Switched Systems

This dissertation includes two main parts. In the first part, the main contribution is to use an inverse optimality approach to analytically solve the Hamilton-Jacobi-Bellman equation of a third order nonlinear optimal control problem for which the dynamics are affine and the cost is quadratic in the input. One special advantage of this work is that the solution is directly obtained for the control input without finding a value function first. However, the value function can be obtained after one solves for the control input and it is shown to be at least a local Lyapunov function. Furthermore, the developed controller is combined with a Continuous-Discrete Extended Kalman Filter (CDEKF) as an approach to deal with noisy measurements and provide an estimate of the states for feedback. The proposed technique is illustrated by its application to a path following problem of a Wheeled Mobile Robot (WMR). The main contribution of the second part of this thesis is the development of two recursive state estimation algorithms for discrete-time piecewise affine (PWA) singular systems with simulation evidence that the idea works for both uncorrelated and correlated process and measurement noise. The proposed algorithms are derived based on successive QR decompositions and Maximum Likelihood (ML) estimation theory. Numerical examples are presented for the case of a PWA system with an unknown input, transformed to a PWA singular system

Cyber-Physical Systems Enabled By Unmanned Aerial System-Based Personal Remote Sensing: Data Mission Quality-Centric Design Architectures

In the coming 20 years, unmanned aerial data collection will be of great importance to many sectors of civilian life. Of these systems, Personal Remote Sensing (PRS) Small Unmanned Aerial Systems (sUASs), which are designed for scientic data collection, will need special attention due to their low cost and high value for farming, scientic, and search-andrescue uses, among countless others. Cyber-Physical Systems (CPSs: large-scale, pervasive automated systems that tightly couple sensing and actuation through technology and the environment) can use sUASs as sensors and actuators, leading to even greater possibilities for benet from sUASs. However, this nascent robotic technology presents as many problems as possibilities due to the challenges surrounding the abilities of these systems to perform safely and eectively for personal, academic, and business use. For these systems, whose missions are dened by the data they are sent to collect, safe and reliable mission quality is of highest importance. Much like the dawning of civil manned aviation, civilian sUAS ights demand privacy, accountability, and other ethical factors for societal integration, while safety of the civilian National Airspace (NAS) is always of utmost importance. While the growing popularity of this technology will drive a great effort to integrate sUASs into the NAS, the only long-term solution to this integration problem is one of proper architecture. In this research, a set of architectural requirements for this integration is presented: the Architecture for Ethical Aerial Information Sensing or AERIS. AERIS provides a cohesive set of requirements for any architecture or set of architectures designed for safe, ethical, accurate aerial data collection. In addition to an overview and showcase of possibilities for sUAS-enabled CPSs, specific examples of AERIS-compatible sUAS architectures using various aerospace design methods are shown. Technical contributions include specic improvements to sUAS payload architecture and control software, inertial navigation and complementary lters, and online energy and health state estimation for lithium-polymer batteries in sUAS missions. Several existing sUASs are proled for their ability to comply with AERIS, and the possibilities of AERIS data-driven missions overall is addressed

Location-Based Sensor Fusion for UAS Urban Navigation.

For unmanned aircraft systems (UAS) to effectively conduct missions in urban environments, a multi-sensor navigation scheme must be developed that can operate in areas with degraded Global Positioning System (GPS) signals. This thesis proposes a sensor fusion plug and play capability for UAS navigation in urban environments to test combinations of sensors. Measurements are fused using both the Extended Kalman Filter (EKF) and Ensemble Kalman Filter (EnKF), a type of Particle Filter. A Long Term Evolution (LTE) transceiver and computer vision sensor each augment the traditional GPS receiver, inertial sensors, and air data system. Availability and accuracy information for each sensor is extracted from the literature. LTE positioning is motivated by a perpetually expanding network that can provide persistent measurements in the urban environment. A location-based logic model is proposed to predict sensor availability and accuracy for a given type of urban environment based on a map database as well as real-time sensor inputs and filter outputs. The simulation is executed in MATLAB where the vehicle dynamics, environment, sensors, and filters are user-customizable. Results indicate that UAS horizontal position accuracy is most dependent on availability of high sampling rate position measurements along with GPS measurement availability. Since the simulation is able to accept LTE sensor specifications, it will be able to show how the UAS position accuracy can be improved in the future with this persistent measurement, even though the accuracy is not improved using current LTE state-of-the-art. In the unmatched true propagation and filter dynamics model scenario, filter tuning proves to be difficult as GPS availability varies from urban canyon to urban canyon. The main contribution of this thesis is the generation of accuracy data for different sensor suites in both a homogeneous urban environment (solid walls) using matched dynamics models and a heterogeneous urban environment layout using unmatched models that necessitate filter tuning. Future work should explore the use of downward facing VISION sensors and LiDAR, integrate real-time map information into sensor availability and measurement weighting decisions, including the use of LTE for approximate localization, and more finely represent expected measurement accuracies in the GPS and LTE networks.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/110361/1/jrufa_1.pd