Modeling and Estimation for Maneuvering Target Tracking with Inertial Systems using Interacting Multiple Models

Projecte final de carrea realitzat en col.laboracio amb Centre Tecnològic de Telecomunicacions de CatalunyaThe aim of this Thesis is to study and develop Estimation Technique that enhances the Dynamic Tracking capability of Maneuvering Targets based using Inertial Systems. Inertial Measurement Systems have measurement biases and drifts and properly estimating their errors is a real time problem. Moreover, different targets perform different types of maneuvers during different stages of their trajectory and as such it is not possible to obtain accurate tracking of target maneuvers using a filters based on conventional single model approach. As such, a technique is required which is dynamic in both estimating and filtering the errors in inertial measurements and in switching to appropriate motion models according to the current maneuver of the vehicle. This thesis suggests and evaluates ‘Interacting Multiple Models (IMM)’ scheme for the solution to the above problem. Performance of the IMM scheme is proven over conventional single model based filters like Kalman Filter through both simulations and real target tracking

A FAULT TOLERANT, DATA FUSION SYSTEM FOR NAVIGATION APPLICATIONS TO A DUCTED FAN VTOL UAV

A Fault Tolerant, Data Fusion (FTDF) algorithm for a Ducted Fan Unmanned Aerial Vehicle (DFUAV) Navigation System is presented. The algorithm have two parts: Gradient Descent (GD) for the Attitude and Heading Reference System (AHRS) and an Interacting Multiple Model (IMM) for position estimation. The GD methodology was designed to fuse the gyroscope, accelerometer, and geomagnetic sensors. The IMM algorithm is able to identify and compensate for multiple sensors data failures. There are three parts in the presentation. Firstly, system identification and the Allan Variance method is used to build dynamic models and noise models for multiple Sensors and Actuators. Secondly, a GD filter is developed for application to the Inertial Measurement Unit (IMU) consisting of tri-axis gyroscopes, accelerometers and magnetometers. The GD filter implementation incorporates magnetic distortion and gyroscope bias drift compensation. The filter uses a quaternion representation, allowing accelerometer and magnetometer data to be used in an analytically derived and optimized algorithm to compute the direction of the gyroscope measurement error as a quaternion derivative. . Finally, the IMM algorithm is used to combine data from multiple sensors simultaneously. This filter uses multiple models that incorporate sensor failures. The probabilities of these models being correct is generated by the IMM. These probabilities can be used to identify sensor failures and compensate for these failures

Dynamic Switching State Systems for Visual Tracking

This work addresses the problem of how to capture the dynamics of maneuvering objects for visual tracking. Towards this end, the perspective of recursive Bayesian filters and the perspective of deep learning approaches for state estimation are considered and their functional viewpoints are brought together

Control algorithms for aerobraking in the Martian atmosphere

The Analytic Predictor Corrector (APC) and Energy Controller (EC) atmospheric guidance concepts were adapted to control an interplanetary vehicle aerobraking in the Martian atmosphere. Changes are made to the APC to improve its robustness to density variations. These changes include adaptation of a new exit phase algorithm, an adaptive transition velocity to initiate the exit phase, refinement of the reference dynamic pressure calculation and two improved density estimation techniques. The modified controller with the hybrid density estimation technique is called the Mars Hybrid Predictor Corrector (MHPC), while the modified controller with a polynomial density estimator is called the Mars Predictor Corrector (MPC). A Lyapunov Steepest Descent Controller (LSDC) is adapted to control the vehicle. The LSDC lacked robustness, so a Lyapunov tracking exit phase algorithm is developed to guide the vehicle along a reference trajectory. This algorithm, when using the hybrid density estimation technique to define the reference path, is called the Lyapunov Hybrid Tracking Controller (LHTC). With the polynomial density estimator used to define the reference trajectory, the algorithm is called the Lyapunov Tracking Controller (LTC). These four new controllers are tested using a six degree of freedom computer simulation to evaluate their robustness. The MHPC, MPC, LHTC, and LTC show dramatic improvements in robustness over the APC and EC

Dynamic Switching State Systems for Visual Tracking

This work addresses the problem of how to capture the dynamics of maneuvering objects for visual tracking. Towards this end, the perspective of recursive Bayesian filters and the perspective of deep learning approaches for state estimation are considered and their functional viewpoints are brought together

Autonomous integrated GPS/INS navigation experiment for OMV. Phase 1: Feasibility study

The phase 1 research focused on the experiment definition. A tightly integrated Global Positioning System/Inertial Navigation System (GPS/INS) navigation filter design was analyzed and was shown, via detailed computer simulation, to provide precise position, velocity, and attitude (alignment) data to support navigation and attitude control requirements of future NASA missions. The application of the integrated filter was also shown to provide the opportunity to calibrate inertial instrument errors which is particularly useful in reducing INS error growth during times of GPS outages. While the Orbital Maneuvering Vehicle (OMV) provides a good target platform for demonstration and for possible flight implementation to provide improved capability, a successful proof-of-concept ground demonstration can be obtained using any simulated mission scenario data, such as Space Transfer Vehicle, Shuttle-C, Space Station