An Integrity Framework for Image-Based Navigation Systems

This work first examines fundamental differences between measurement models established for GPS and those of proposed image-based navigation systems. In contrast to single value per satellite GPS pseudorange measurements, image measurements are inherently angle-based and represent pixel coordinate pairs for each mapped target. Thus, in the image-based case, special consideration must be given to the units of the transformations between the states and measurements, and also to the fact that multiple rows of the observation matrix relate to particular error states. An algorithm is developed to instantiate a framework for image-based integrity analogous to that of GPS RAIM. The algorithm is applied cases where the navigation system is estimating position only and then extended to cases where both position and attitude estimation is required. Detailed analysis demonstrates the impact of angular error on a single pixel pair measurement and comparisons from both estimation scenario results show that, from an integrity perspective, there is significant benefit in having known attitude information. Additional work demonstrates the impact of pixel pair measurement relative geometries on system integrity, showing potential improvement in image-based integrity through screening and adding measurements, when available, to the navigation system solution

Anti-disturbance fault tolerant initial alignment for inertial navigation system subjected to multiple disturbances

Modeling error, stochastic error of inertial sensor, measurement noise and environmental disturbance affect the accuracy of an inertial navigation system (INS). In addition, some unpredictable factors, such as system fault, directly affect the reliability of INSs. This paper proposes a new anti-disturbance fault tolerant alignment approach for a class of INSs sub- jected to multiple disturbances and system faults. Based on modeling and error analysis, stochastic error of inertial sensor, measurement noise, modeling error and environmental disturbance are formulated into different types of disturbances described by a Markov stochastic process, Gaussian noise and a norm-bounded variable, respectively. In order to improve the accuracy and reliability of an INS, an anti-disturbance fault tolerant filter is designed. Then, a mixed dissipative/guarantee cost performance is applied to attenuate the norm-bounded disturbance and to optimize the estimation error. Slack variables and dissipativeness are introduced to reduce the conservatism of the proposed approach. Finally, compared with the unscented Kalman filter (UKF), simulation results for self-alignment of an INS are provided based on experimental data. It can be shown that the proposed method has an enhanced disturbance rejection and attenuation performance with high reliability

Design and implementation of resilient attitude estimation algorithms for aerospace applications

Satellite attitude estimation is a critical component of satellite attitude determination and control systems, relying on highly accurate sensors such as IMUs, star trackers, and sun sensors. However, the complex space environment can cause sensor performance degradation or even failure. To address this issue, FDIR systems are necessary. This thesis presents a novel approach to satellite attitude estimation that utilizes an InertialNavigation System (INS) to achieve high accuracy with the low computational load. The algorithm is based on a two-layer Kalman filter, which incorporates the quaternion estimator(QUEST) algorithm, FQA, Linear interpolation (LERP)algorithms, and KF. Moreover, the thesis proposes an FDIR system for the INS that can detect and isolate faults and recover the system safely. This system includes two-layer fault detection with isolation and two-layered recovery, which utilizes an Adaptive Unscented Kalman Filter (AUKF), QUEST algorithm, residual generators, Radial Basis Function (RBF) neural networks, and an adaptive complementary filter (ACF). These two fault detection layers aim to isolate and identify faults while decreasing the rate of false alarms. An FPGA-based FDIR system is also designed and implemented to reduce latency while maintaining normal resource consumption in this thesis. Finally, a Fault Tolerance Federated Kalman Filter (FTFKF) is proposed to fuse the output from INS and the CNS to achieve high precision and robust attitude estimation.The findings of this study provide a solid foundation for the development of FDIR systems for various applications such as robotics, autonomous vehicles, and unmanned aerial vehicles, particularly for satellite attitude estimation. The proposed INS-based approach with the FDIR system has demonstrated high accuracy, fault tolerance, and low computational load, making it a promising solution for satellite attitude estimation in harsh space environment

A scalable, portable, FPGA-based implementation of the Unscented Kalman Filter

Sustained technological progress has come to a point where robotic/autonomous systems may well soon become ubiquitous. In order for these systems to actually be useful, an increase in autonomous capability is necessary for aerospace, as well as other, applications. Greater aerospace autonomous capability means there is a need for high performance state estimation. However, the desire to reduce costs through simplified development processes and compact form factors can limit performance. A hardware-based approach, such as using a Field Programmable Gate Array (FPGA), is common when high performance is required, but hardware approaches tend to have a more complicated development process when compared to traditional software approaches; greater development complexity, in turn, results in higher costs. Leveraging the advantages of both hardware-based and software-based approaches, a hardware/software (HW/SW) codesign of the Unscented Kalman Filter (UKF), based on an FPGA, is presented. The UKF is split into an application-specific part, implemented in software to retain portability, and a non-application-specific part, implemented in hardware as a parameterisable IP core to increase performance. The codesign is split into three versions (Serial, Parallel and Pipeline) to provide flexibility when choosing the balance between resources and performance, allowing system designers to simplify the development process. Simulation results demonstrating two possible implementations of the design, a nanosatellite application and a Simultaneous Localisation and Mapping (SLAM) application, are presented. These results validate the performance of the HW/SW UKF and demonstrate its portability, particularly in small aerospace systems. Implementation (synthesis, timing, power) details for a variety of situations are presented and analysed to demonstrate how the HW/SW codesign can be scaled for any application

Dynamics and Control of Satellite Relative Motion in Proximity Operations

In this dissertation, the development of relative navigation, guidance, and control algorithms of an autonomous space rendezvous and docking system are presented. These algorithms are based on innovative formulations of the relative motion equations that are completely explicit in time. The navigation system uses an extended Kalman filter based on these formulations to estimate the relative position and velocity of the chaser vehicle with respect to the target vehicle and the chaser attitude and gyro biases. This filter uses the range and angle measurements of the target relative to the chaser from a simulated LIDAR system, along with the star tracker and gyro measurements of the chaser. The corresponding measurement models, process noise matrix, and other filter parameters are provided. The guidance and control algorithms are based on the glideslope used in the past for rendezvous and proximity operations of the Space Shuttle with other vehicles. These algorithms are used to approach, flyaround, and to depart from a target vehicle in elliptic orbits. The algorithms are general and able to translate the chaser vehicle in any direction, decelerate while approaching the target vehicle, and accelerate when moving away. Numerical nonlinear simulations that illustrate the relative navigation, attitude estimation, guidance, and control algorithm\u27s, as well as performance and accuracy are evaluated in the research study