Thesis by Cory T Fraser
To enhance the capabilities of onboard autonomous guidance, navigation and control systems, this ... more To enhance the capabilities of onboard autonomous guidance, navigation and control systems, this thesis presents the development of two adaptive extended Kalman filter navigation algorithms for spacecraft formation flying. The proposed adaptive filters are capable of updating the internal noise characteristics of the Kalman filter in real time, and are viable in all orbit scenarios, including highly elliptical orbits in the presence of perturbations. The first Kalman filter approach uses maximum likelihood estimation techniques to derive analytical adaptations laws for the filter, and the second approach uses an embedded fuzzy logic system based on a covariance-matching analysis of the filter residuals. Numerical simulations of three spacecraft formations are used to demonstrate that the proposed adaptive navigation algorithms are appreciably more robust to filter initialization errors, dynamics modelling deficiencies, and measurement noise than the standard extended Kalman filter.
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Journal Papers by Cory T Fraser
Acta Astronautica, 2020
The relative navigation problem for spacecraft formation flying missions in near-Earth orbit is a... more The relative navigation problem for spacecraft formation flying missions in near-Earth orbit is addressed here
through the design of two unique adaptive extended Kalman filter algorithms. The adaptive filters are capable
of updating the internal noise characteristics of the Kalman filter in real time, and are viable in all orbit
scenarios, including elliptical orbits subjected to perturbations. The first adaptive Kalman filter approach uses maximum likelihood estimation techniques to derive analytical adaptations laws, which are then improved
through the novel inclusion of an intrinsic smoothing routine. The second approach uses an embedded fuzzy
logic system based on a covariance-matching analysis of the filter residuals, where the fuzzy system has been
specifically designed for the spacecraft navigation problem at hand. Numerical simulations of two spacecraft
formations demonstrate that the proposed adaptive navigation algorithms are appreciably more robust to filter
initialization errors, dynamics modelling deficiencies, and measurement noises than the standard Kalman filter.
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Conference Papers by Cory T Fraser
American Control Conference (ACC), 2019
Over the past two decades, advances in spacecraft technologies have prompted the development of a... more Over the past two decades, advances in spacecraft technologies have prompted the development of autonomous onboard navigation systems. This paper presents the design of a novel Fuzzy Adaptive Extended Kalman Filter (FAEKF) suitable for estimating the relative position and velocity between two spacecraft flying in formation. A fuzzy adaptation architecture is embedded within a standard Extended Kalman Filter (EKF), thereby allowing the filter to adapt internal noise characteristics that would otherwise remain constant after the initial filter design. Inaccurate tuning of the process and measurement noise covariance matrices within an EKF are commonly a limiting factor in the estimation performance, especially in situations where the behaviour of the noise processes are poorly defined or subject to change. In this context, the proposed approach provides a method to update the process and measurement noise covariances online based on a covariance-matching analysis of the filter residuals. A demonstration of the technique is given through numerical simulations of a spacecraft formation in low-Earth orbit, which are used to compare state estimates from the FAEKF with those from measurement-only and non-adaptive EKF solutions.
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American Control Conference (ACC), 2018
In the interests of enhancing autonomous navigation capabilities for Low Earth Orbit formation fl... more In the interests of enhancing autonomous navigation capabilities for Low Earth Orbit formation flying, this work presents the development of an Adaptive Extended Kalman Filter (AEKF) that estimates relative position and velocity states between two spacecraft. A standard EKF based on the nonlinear dynamics of relative motion is used to provide preliminary state estimates of the formation, which are then corrected through a fixed-window smoothing routine. Since uncertainties in the process and measurement noise covariances within the filter inherently limit the final accuracy of the EKF, an online tuning mechanism is derived using Maximum Likelihood Estimation (MLE) to optimize the noise covariances given an available set of measurements. Inclusion of these adaptations improves filter robustness by allowing the filter to handle situations where noise characteristics of the system are unknown or subject to change, while simultaneously eliminating the need for the initial manual covariance tuning process that accompanies EKF design. Numerical validation of the proposed algorithm is completed by comparing navigation solutions from the AEKF with those obtained from the non-adaptive EKF, using a realistic in-plane elliptical spacecraft formation.
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Papers by Cory T Fraser
2018 Annual American Control Conference (ACC), 2018
In the interests of enhancing autonomous navigation capabilities for Low Earth Orbit formation fl... more In the interests of enhancing autonomous navigation capabilities for Low Earth Orbit formation flying, this work presents the development of an Adaptive Extended Kalman Filter (AEKF) that estimates relative position and velocity states between two spacecraft. A standard EKF based on the nonlinear dynamics of relative motion is used to provide preliminary state estimates of the formation, which are then corrected through a fixed-window smoothing routine. Since uncertainties in the process and measurement noise covariances within the filter inherently limit the final accuracy of the EKF, an online tuning mechanism is derived using Maximum Likelihood Estimation (MLE) to optimize the noise covariances given an available set of measurements. Inclusion of these adaptations improves filter robustness by allowing the filter to handle situations where noise characteristics of the system are unknown or subject to change, while simultaneously eliminating the need for the initial manual covariance tuning process that accompanies EKF design. Numerical validation of the proposed algorithm is completed by comparing navigation solutions from the AEKF with those obtained from the non-adaptive EKF, using a realistic in-plane elliptical spacecraft formation.
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2019 American Control Conference (ACC)
Over the past two decades, advances in spacecraft technologies have prompted the development of a... more Over the past two decades, advances in spacecraft technologies have prompted the development of autonomous onboard navigation systems. This paper presents the design of a novel Fuzzy Adaptive Extended Kalman Filter (FAEKF) suitable for estimating the relative position and velocity between two spacecraft flying in formation. A fuzzy adaptation architecture is embedded within a standard Extended Kalman Filter (EKF), thereby allowing the filter to adapt internal noise characteristics that would otherwise remain constant after the initial filter design. Inaccurate tuning of the process and measurement noise covariance matrices within an EKF are commonly a limiting factor in the estimation performance, especially in situations where the behaviour of the noise processes are poorly defined or subject to change. In this context, the proposed approach provides a method to update the process and measurement noise covariances online based on a covariance-matching analysis of the filter residuals. A demonstration of the technique is given through numerical simulations of a spacecraft formation in low-Earth orbit, which are used to compare state estimates from the FAEKF with those from measurement-only and non-adaptive EKF solutions.
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Acta Astronautica, 2021
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Thesis by Cory T Fraser
Journal Papers by Cory T Fraser
through the design of two unique adaptive extended Kalman filter algorithms. The adaptive filters are capable
of updating the internal noise characteristics of the Kalman filter in real time, and are viable in all orbit
scenarios, including elliptical orbits subjected to perturbations. The first adaptive Kalman filter approach uses maximum likelihood estimation techniques to derive analytical adaptations laws, which are then improved
through the novel inclusion of an intrinsic smoothing routine. The second approach uses an embedded fuzzy
logic system based on a covariance-matching analysis of the filter residuals, where the fuzzy system has been
specifically designed for the spacecraft navigation problem at hand. Numerical simulations of two spacecraft
formations demonstrate that the proposed adaptive navigation algorithms are appreciably more robust to filter
initialization errors, dynamics modelling deficiencies, and measurement noises than the standard Kalman filter.
Conference Papers by Cory T Fraser
Papers by Cory T Fraser
through the design of two unique adaptive extended Kalman filter algorithms. The adaptive filters are capable
of updating the internal noise characteristics of the Kalman filter in real time, and are viable in all orbit
scenarios, including elliptical orbits subjected to perturbations. The first adaptive Kalman filter approach uses maximum likelihood estimation techniques to derive analytical adaptations laws, which are then improved
through the novel inclusion of an intrinsic smoothing routine. The second approach uses an embedded fuzzy
logic system based on a covariance-matching analysis of the filter residuals, where the fuzzy system has been
specifically designed for the spacecraft navigation problem at hand. Numerical simulations of two spacecraft
formations demonstrate that the proposed adaptive navigation algorithms are appreciably more robust to filter
initialization errors, dynamics modelling deficiencies, and measurement noises than the standard Kalman filter.