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Papers by Benjamin Brunson

Research paper thumbnail of Innovative Formulation in Discrete Kalman Filtering with Constraints - A Generic Framework for Comprehensive Error Analysis

Journal of global positioning systems/Journal of Global Positioning Systems, 2022

This manuscript establishes a generic framework for comprehensive error analysis in discrete Kalm... more This manuscript establishes a generic framework for comprehensive error analysis in discrete Kalman filtering with constraints, which systematically provides a complete set of algorithmic formulas along with demonstrating an alternative process of theoretical analytics of discrete Kalman filter. This constructive work aims extensively to standardize the formulation of Kalman filter with constraints. In analogy to the similar framework for standard discrete Kalman filter (without any constraints), the proposed framework specifically considers: model formulation vs. the error sources, the solution of the state and process noise vectors, the residuals for the process noise vector and the measurement noise vector, the redundancy contribution of the predicted state vector, process noise vector and measurement vector, and other relevant essential aspects, of which some of the features are essential to comprehensive error analysis, but are nonexistent yet in the primary algorithm in Kalman filtering with constraints. Besides, the algorithmic form of the Extended Kalman filter with constraints is also provided for practical purpose. At the end, specific remarks about the developed framework are given to emphasize on its usage to a certain extend.

Research paper thumbnail of Three Attitude Models and their Characterization in the Generic Multisensor Integration Strategy for Kinematic Positioning and Navigation

Journal of global positioning systems/Journal of Global Positioning Systems, 2023

This research aims at further completing our novel Generic Multisensor Integration Strategy (GMIS... more This research aims at further completing our novel Generic Multisensor Integration Strategy (GMIS) with the systematic development of three alternate attitude models, i.e., roll-pitch-heading (RPH), direction cosine matrix (DCM), and quaternion. The GMIS' potential for a true sensor level data fusion is leveraged to its full extent here by facilitating comprehensive error analysis framework in Kalman filtering. A comparative analysis between the solutions resulted from the GMIS associated with each attitude model have been analysed and compared through real road test data. The attitude models were found to perform very consistently, exhibiting the same behaviours in the residuals of the process noise and measurement vectors along with the estimated variance components. Besides, an analysis was conducted to investigate how each attitude model reacts to a sudden trajectory variation captured by the IMU. Each attitude model still performed consistently, but the DCM model in particular exhibited resistance to absorbing erroneous observations into its process noise estimates.

Research paper thumbnail of SAR Phase Unwrapping Using Path-Based Least-Squares Phase Estimation and Region-Growing with Polynomial-Based Phase Prediction

Differential SAR interferometry (DInSAR) has proven to be a processing approach that is wellsuite... more Differential SAR interferometry (DInSAR) has proven to be a processing approach that is wellsuited to precisely identifying large-scale land deformation patterns. This is useful for many environmental monitoring applications, but the speckle noise and temporal decorrelation present in SAR images presents particular challenges in processing SAR images. This research focuses on the phase unwrapping problem, proposing two new approaches: Polynomial-Based Region-Growing Phase Unwrapping (PBRGPU), which expands upon the traditional region-growing approach to phase unwrapping; and Path-Based Least-Squares Phase Unwrapping (PBLSPU), which extends the leastsquares phase unwrapping models in a path-based framework. Both algorithms were tested using simulated data and interferograms generated from RADARSAT-2 data. Both approaches significantly reduced the root mean square error compared to the algorithms they build from, and achieved a similar level of performance to the commonly-used SNAPHU algorithm without the need for masking low coherence areas. iii Acknowledgements I would like to express appreciation to York University, NSERC, the Canadian Space Agency, and PCI Geomatics for providing financial support for this research. In addition, I would like to thank the Canadian Space Agency for providing the SAR data used in this research and PCI Geomatics for the use of their software and technical support services. I would like to thank my supervisor, Professor Jianguo Wang, and my supervisory committee member, Professor Baoxin Hu, for the support they have provided throughout this research and through all my studies. I would like to extend my thanks to Professor Mojgan Jadidi and Professor Juejiao Fu for having served on my M.A.Sc. defense committee. I would also like to thank my family for supporting and encouraging me through my studies.

Research paper thumbnail of Synthetic aperture radar phase unwrapping using region-growing with polynomial-based phase prediction

Geomatica, 2020

Phase unwrapping for interferometric synthetic aperture radar (InSAR) remains a challenge due to ... more Phase unwrapping for interferometric synthetic aperture radar (InSAR) remains a challenge due to the presence of speckle noise and temporal decorrelation in many interferograms. This paper proposes a polynomial-based region-growing phase unwrapping (PBRGPU) approach that is built on the region-growing phase unwrapping (RGPU) approach in Xu and Cumming (Xu and Cumming. 1996. A region growing algorithm for insar phase unwrapping. IGARSS 96. International Geoscience and Remote Sensing Symposium. 31–31 May 1996. Lincoln, NE, USA. doi: 10.1109/igarss.1996.516883 ). The proposed approach iteratively performs phase unwrapping at the edges of multiple seeded regions using a least-squares polynomial phase prediction, which allows for the use of statistically rigorous quality assurance to remove low quality pixels from further processing. Here, a user-specified statistical confidence interval is more intuitive to users than the threshold parameters used by other algorithms. The proposed appro...

Research paper thumbnail of SAR Phase Unwrapping Using Path-Based Least-Squares Phase Estimation and Region-Growing with Polynomial-Based Phase Prediction

Differential SAR interferometry (DInSAR) has proven to be a processing approach that is wellsuite... more Differential SAR interferometry (DInSAR) has proven to be a processing approach that is wellsuited to precisely identifying large-scale land deformation patterns. This is useful for many environmental monitoring applications, but the speckle noise and temporal decorrelation present in SAR images presents particular challenges in processing SAR images. This research focuses on the phase unwrapping problem, proposing two new approaches: Polynomial-Based Region-Growing Phase Unwrapping (PBRGPU), which expands upon the traditional region-growing approach to phase unwrapping; and Path-Based Least-Squares Phase Unwrapping (PBLSPU), which extends the leastsquares phase unwrapping models in a path-based framework. Both algorithms were tested using simulated data and interferograms generated from RADARSAT-2 data. Both approaches significantly reduced the root mean square error compared to the algorithms they build from, and achieved a similar level of performance to the commonly-used SNAPHU algorithm without the need for masking low coherence areas. iii Acknowledgements I would like to express appreciation to York University, NSERC, the Canadian Space Agency, and PCI Geomatics for providing financial support for this research. In addition, I would like to thank the Canadian Space Agency for providing the SAR data used in this research and PCI Geomatics for the use of their software and technical support services. I would like to thank my supervisor, Professor Jianguo Wang, and my supervisory committee member, Professor Baoxin Hu, for the support they have provided throughout this research and through all my studies. I would like to extend my thanks to Professor Mojgan Jadidi and Professor Juejiao Fu for having served on my M.A.Sc. defense committee. I would also like to thank my family for supporting and encouraging me through my studies.

Research paper thumbnail of Innovative Formulation in Discrete Kalman Filtering with Constraints - A Generic Framework for Comprehensive Error Analysis

Journal of global positioning systems/Journal of Global Positioning Systems, 2022

This manuscript establishes a generic framework for comprehensive error analysis in discrete Kalm... more This manuscript establishes a generic framework for comprehensive error analysis in discrete Kalman filtering with constraints, which systematically provides a complete set of algorithmic formulas along with demonstrating an alternative process of theoretical analytics of discrete Kalman filter. This constructive work aims extensively to standardize the formulation of Kalman filter with constraints. In analogy to the similar framework for standard discrete Kalman filter (without any constraints), the proposed framework specifically considers: model formulation vs. the error sources, the solution of the state and process noise vectors, the residuals for the process noise vector and the measurement noise vector, the redundancy contribution of the predicted state vector, process noise vector and measurement vector, and other relevant essential aspects, of which some of the features are essential to comprehensive error analysis, but are nonexistent yet in the primary algorithm in Kalman filtering with constraints. Besides, the algorithmic form of the Extended Kalman filter with constraints is also provided for practical purpose. At the end, specific remarks about the developed framework are given to emphasize on its usage to a certain extend.

Research paper thumbnail of Three Attitude Models and their Characterization in the Generic Multisensor Integration Strategy for Kinematic Positioning and Navigation

Journal of global positioning systems/Journal of Global Positioning Systems, 2023

This research aims at further completing our novel Generic Multisensor Integration Strategy (GMIS... more This research aims at further completing our novel Generic Multisensor Integration Strategy (GMIS) with the systematic development of three alternate attitude models, i.e., roll-pitch-heading (RPH), direction cosine matrix (DCM), and quaternion. The GMIS' potential for a true sensor level data fusion is leveraged to its full extent here by facilitating comprehensive error analysis framework in Kalman filtering. A comparative analysis between the solutions resulted from the GMIS associated with each attitude model have been analysed and compared through real road test data. The attitude models were found to perform very consistently, exhibiting the same behaviours in the residuals of the process noise and measurement vectors along with the estimated variance components. Besides, an analysis was conducted to investigate how each attitude model reacts to a sudden trajectory variation captured by the IMU. Each attitude model still performed consistently, but the DCM model in particular exhibited resistance to absorbing erroneous observations into its process noise estimates.

Research paper thumbnail of SAR Phase Unwrapping Using Path-Based Least-Squares Phase Estimation and Region-Growing with Polynomial-Based Phase Prediction

Differential SAR interferometry (DInSAR) has proven to be a processing approach that is wellsuite... more Differential SAR interferometry (DInSAR) has proven to be a processing approach that is wellsuited to precisely identifying large-scale land deformation patterns. This is useful for many environmental monitoring applications, but the speckle noise and temporal decorrelation present in SAR images presents particular challenges in processing SAR images. This research focuses on the phase unwrapping problem, proposing two new approaches: Polynomial-Based Region-Growing Phase Unwrapping (PBRGPU), which expands upon the traditional region-growing approach to phase unwrapping; and Path-Based Least-Squares Phase Unwrapping (PBLSPU), which extends the leastsquares phase unwrapping models in a path-based framework. Both algorithms were tested using simulated data and interferograms generated from RADARSAT-2 data. Both approaches significantly reduced the root mean square error compared to the algorithms they build from, and achieved a similar level of performance to the commonly-used SNAPHU algorithm without the need for masking low coherence areas. iii Acknowledgements I would like to express appreciation to York University, NSERC, the Canadian Space Agency, and PCI Geomatics for providing financial support for this research. In addition, I would like to thank the Canadian Space Agency for providing the SAR data used in this research and PCI Geomatics for the use of their software and technical support services. I would like to thank my supervisor, Professor Jianguo Wang, and my supervisory committee member, Professor Baoxin Hu, for the support they have provided throughout this research and through all my studies. I would like to extend my thanks to Professor Mojgan Jadidi and Professor Juejiao Fu for having served on my M.A.Sc. defense committee. I would also like to thank my family for supporting and encouraging me through my studies.

Research paper thumbnail of Synthetic aperture radar phase unwrapping using region-growing with polynomial-based phase prediction

Geomatica, 2020

Phase unwrapping for interferometric synthetic aperture radar (InSAR) remains a challenge due to ... more Phase unwrapping for interferometric synthetic aperture radar (InSAR) remains a challenge due to the presence of speckle noise and temporal decorrelation in many interferograms. This paper proposes a polynomial-based region-growing phase unwrapping (PBRGPU) approach that is built on the region-growing phase unwrapping (RGPU) approach in Xu and Cumming (Xu and Cumming. 1996. A region growing algorithm for insar phase unwrapping. IGARSS 96. International Geoscience and Remote Sensing Symposium. 31–31 May 1996. Lincoln, NE, USA. doi: 10.1109/igarss.1996.516883 ). The proposed approach iteratively performs phase unwrapping at the edges of multiple seeded regions using a least-squares polynomial phase prediction, which allows for the use of statistically rigorous quality assurance to remove low quality pixels from further processing. Here, a user-specified statistical confidence interval is more intuitive to users than the threshold parameters used by other algorithms. The proposed appro...

Research paper thumbnail of SAR Phase Unwrapping Using Path-Based Least-Squares Phase Estimation and Region-Growing with Polynomial-Based Phase Prediction

Differential SAR interferometry (DInSAR) has proven to be a processing approach that is wellsuite... more Differential SAR interferometry (DInSAR) has proven to be a processing approach that is wellsuited to precisely identifying large-scale land deformation patterns. This is useful for many environmental monitoring applications, but the speckle noise and temporal decorrelation present in SAR images presents particular challenges in processing SAR images. This research focuses on the phase unwrapping problem, proposing two new approaches: Polynomial-Based Region-Growing Phase Unwrapping (PBRGPU), which expands upon the traditional region-growing approach to phase unwrapping; and Path-Based Least-Squares Phase Unwrapping (PBLSPU), which extends the leastsquares phase unwrapping models in a path-based framework. Both algorithms were tested using simulated data and interferograms generated from RADARSAT-2 data. Both approaches significantly reduced the root mean square error compared to the algorithms they build from, and achieved a similar level of performance to the commonly-used SNAPHU algorithm without the need for masking low coherence areas. iii Acknowledgements I would like to express appreciation to York University, NSERC, the Canadian Space Agency, and PCI Geomatics for providing financial support for this research. In addition, I would like to thank the Canadian Space Agency for providing the SAR data used in this research and PCI Geomatics for the use of their software and technical support services. I would like to thank my supervisor, Professor Jianguo Wang, and my supervisory committee member, Professor Baoxin Hu, for the support they have provided throughout this research and through all my studies. I would like to extend my thanks to Professor Mojgan Jadidi and Professor Juejiao Fu for having served on my M.A.Sc. defense committee. I would also like to thank my family for supporting and encouraging me through my studies.