자료유형  

 학위논문

Control Number  

0017163509

International Standard Book Number  

9798384053699

Dewey Decimal Classification Number  

629.1

Main Entry-Personal Name  

Oliver, Rachel.

Publication, Distribution, etc. (Imprint  

[S.l.] : Cornell University., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

385 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

General Note  

Advisor: Savransky, Dmitry.

Dissertation Note  

Thesis (Ph.D.)--Cornell University, 2024.

Summary, Etc.  

요약Event-based vision sensors (EVS) provide a unique opportunity for Space Domain Awareness (SDA) applications. Inspired by the human eye, these sensors operate on the principle of change detection and each pixel functions independently and asynchronously from the other pixels. Pixels trigger events when a change in light intensity occurs. The sensor records events as a sparse time series output with microsecond level of precision. The space sensing community is interested in this technology due to wide dynamic range achieved by the operating in the log scale, the minimal data produced for a relatively static scene where changes in intensity are infrequent, and the temporal precision that opens opportunities to capture information on fast moving space objects where traditional frame imaging is not an option. As a relatively inexpensive sensor with technical capabilities well suited for tracking, they could augment existing ground-based systems or be a primary sensor on-board a spacecraft. EVS are uniquely suited for space-based operations. With low size, weight, power, and data requirements, they easily fit into tight engineering budgets for space systems. The data may even be well suited for on-board processing due to its sparsity. Despite all these advantages, the sensors are not ready to implement into SDA operations. Creating algorithms to handle the time series data and optimizing the sensor for low-light imaging are areas of active research to improve the utility of these sensors as tools for SDA. To support these efforts, I develop physics-based end-to-end model for event-based sensing of resident space objects (RSOs). This model adapts previous synthetic event generation methods to operate with photon flux input and precise measures of current. By implementing a model of pixel readout with microsecond-level precision and developing methods to model noise based on dark and induced current levels, I improve the accuracy of the frequency and polarity of the events produced. This accuracy is necessary to extrapolate sensor performance from the model and to generate synthetic events to feed algorithmic development. I also contribute to event-based algorithms through development of an online non-frame based tracking algorithm. In order to validate the sensor model and train the classifying portions of my tracking algorithms, I develop batch-based clustering methods that leverage the temporal dimension which improves the labeling of events between star and noise by 31.8%. Through exploration of different grouping and classifying methods for the tracking algorithm, I attain a maximum of 94.5% group agreement with the batch clustered data and a 97.6% true positive rate and 99.9% true negative rate when classifying satellites on a validation data set. Star classification performance is slightly lower at a 96.7% true positive rate and 96.5% true negative rate. The tracking algorithm's success on this one set of data suggests promising performance from these sensors in future SDA applications.

Subject Added Entry-Topical Term  

Aerospace engineering.

Subject Added Entry-Topical Term  

Mechanical engineering.

Subject Added Entry-Topical Term  

Astronomy.

Index Term-Uncontrolled  

Event generation simulation

Index Term-Uncontrolled  

Event-based sensing

Index Term-Uncontrolled  

Neuromorphic sensors

Index Term-Uncontrolled  

Satellite tracking

Index Term-Uncontrolled  

Space Domain Awareness

Index Term-Uncontrolled  

Sparse data classification

Added Entry-Corporate Name  

Cornell University Aerospace Engineering

Host Item Entry  

Dissertations Abstracts International. 86-03B.

Electronic Location and Access  

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Control Number  

joongbu:658539