Nonlinear Navigation System Design with Application to Autonomous Vehicles

This thesis addresses the design of nonlinear navigation systems for autonomous vehicles, following two main approaches: Kalman filter based estimators, and Lyapunov theory based nonlinear observers. The proposed Kalman filter architectures are designed for accurate position and attitude estimation using low-cost sensor suites. An extended Kalman filter is adopted to merge a high accuracy inertial navigation system with advanced aiding information, namely i) frequency contents of vector measurements, ii) vehicle model dynamics, and iii) LASER range measurements. In alternative, simple yet effective multirate complementary Kalman filters are proposed, endowed with stability and performance properties. The navigation systems are validated using realistic vehicle simulators, and experimental data collected onboard the DELFIMx autonomous surface craft. The second approach addresses the design of nonlinear observers in non-Euclidean spaces. The observers are derived resorting to Lyapunov theory, bearing stability and robustness properties in the presence of inertial sensor non-idealities. The considered sensor readings are provided by an inertial measurement unit and i) landmark measurements, ii) vector observations, and iii) GPS receivers. The regions of attraction are explicitly characterized, and an output feedback configuration is proposed, allowing for the practical implementation of the algorithms.