Space-based relative multitarget tracking

Access to space has expanded dramatically over the past decade. The growing popularity of small satellites, specifically cubesats, and the following launch initiatives have resulted in exponentially growing launch numbers into low Earth orbit. This growing congestion in space has punctuated the need for local space monitoring and autonomous satellite inspection. This work describes the development of a framework for monitoring local space and tracking multiple objects concurrently in a satellite\u27s neighborhood. The development of this multitarget tracking systems has produced collateral developments in numerical methods, relative orbital mechanics, and initial relative orbit determination. This work belongs to a class of navigation known as angles-only navigation, in which angles representing the direction to the target are measured but no range measurements are available. A key difference between this work and traditional angles-only relative navigation research is that angle measurements are collected from two separate cameras simultaneously. Such measurements, when coupled with the known location and orientation of the stereo cameras, can be used to resolve the relative range component of a target\u27s position. This fact is exploited to form initial statistical representations of the targets\u27 relative states, which are subsequently refined in Bayesian single-target and multitarget frameworks --Abstract, page iii

Random Finite Set Information-Theoretic Sensor Control for Autonomous Multi-Sensor Multi-Object Surveillance

153 pagesTracking multiple moving objects in complex environments is a key objective of many robotic and aerospace surveillance systems. In the Bayesian multi-object tracking framework, noisy sensor measurements are assimilated over time to form probabilistic beliefs, namely probability densities, of the multi-object state by virtue of Bayes' rule. This dissertation shows that, using probabilistic beliefs and environmental feedback, intelligent sensors can also optimize the value of information gathered in real time by means of information-driven control. In particular, it is shown that in object tracking applications, sensor actions can be optimized based on the expected reduction in uncertainty or information gain estimated from probabilistic beliefs for future sensor measurements. When compared to traditional estimation problems, the problem of estimating the information value for multi-object surveillance is more challenging due to unknown object-measurement association and unknown object existence. The advent of random finite set (RFS) theory has provided a formalism for quantifying and estimating information gain in multi-object tracking problems. However, direct computation of many relevant RFS functions, including posterior density functions and predicted information gain functions, is often intractable and requires principled approximation. This dissertation presents new theory, approximations, and algorithms related to autonomous multi-sensor multi-object surveillance. A new approach is presented for systematically incorporating ambiguous inclusion/exclusion type evidence, such as the non-detection of an object within a known sensor field-of-view (FoV). The resulting state estimation problem is nonlinear and solved using a new Gaussian mixture approximation achieved through recursive component splitting.Based on this approximation, a novel Gaussian mixture Bernoulli filter for imprecise measurements is derived. The filter can accommodate "soft" data from human sources and is demonstrated in a tracking problem using only natural language statements as inputs. This dissertation further investigates the relationship between bounded FoVs and cardinality distributions for a representative selection of multi-object distributions. These new FoV cardinality distributions can be used for sensor planning, as is demonstrated through a problem involving a multi-Bernoulli process with up to one hundred potential objects. Finally, a new tractable approximation is presented for RFS expected information gain that is applicable to sensor control in multi-sensor multi-object search-while-tracking problems. Unlike existing RFS approaches, the approximation presented in this dissertation accounts for multiple measurement outcomes due to noise, missed detections, false alarms, and object appearance/disappearance. The effectiveness of the information-driven sensor control is demonstrated through a multi-vehicle search-while-tracking experiment using real video data from a remote optical sensor.2023-09-0

Snowmass 2021 CMB-S4 White Paper

This Snowmass 2021 White Paper describes the Cosmic Microwave Background Stage 4 project CMB-S4, which is designed to cross critical thresholds in our understanding of the origin and evolution of the Universe, from the highest energies at the dawn of time through the growth of structure to the present day. We provide an overview of the science case, the technical design, and project plan

Snowmass 2021 CMB-S4 White Paper

This Snowmass 2021 White Paper describes the Cosmic Microwave Background Stage 4 project CMB-S4, which is designed to cross critical thresholds in our understanding of the origin and evolution of the Universe, from the highest energies at the dawn of time through the growth of structure to the present day. We provide an overview of the science case, the technical design, and project plan

Snowmass 2021 CMB-S4 White Paper

This Snowmass 2021 White Paper describes the Cosmic Microwave Background Stage 4 project CMB-S4, which is designed to cross critical thresholds in our understanding of the origin and evolution of the Universe, from the highest energies at the dawn of time through the growth of structure to the present day. We provide an overview of the science case, the technical design, and project plan

Snowmass 2021 CMB-S4 White Paper

This Snowmass 2021 White Paper describes the Cosmic Microwave Background Stage 4 project CMB-S4, which is designed to cross critical thresholds in our understanding of the origin and evolution of the Universe, from the highest energies at the dawn of time through the growth of structure to the present day. We provide an overview of the science case, the technical design, and project plan

Snowmass 2021 CMB-S4 White Paper

This Snowmass 2021 White Paper describes the Cosmic Microwave Background Stage 4 project CMB-S4, which is designed to cross critical thresholds in our understanding of the origin and evolution of the Universe, from the highest energies at the dawn of time through the growth of structure to the present day. We provide an overview of the science case, the technical design, and project plan

Snowmass 2021 CMB-S4 White Paper

This Snowmass 2021 White Paper describes the Cosmic Microwave Background Stage 4 project CMB-S4, which is designed to cross critical thresholds in our understanding of the origin and evolution of the Universe, from the highest energies at the dawn of time through the growth of structure to the present day. We provide an overview of the science case, the technical design, and project plan

Snowmass 2021 CMB-S4 White Paper

This Snowmass 2021 White Paper describes the Cosmic Microwave Background Stage 4 project CMB-S4, which is designed to cross critical thresholds in our understanding of the origin and evolution of the Universe, from the highest energies at the dawn of time through the growth of structure to the present day. We provide an overview of the science case, the technical design, and project plan