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An Event-Based Vision Sensor Simulat...

Oliver, Rachel,

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An Event-Based Vision Sensor Simulation Framework for Space Domain Awareness Applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	An Event-Based Vision Sensor Simulation Framework for Space Domain Awareness Applications // Rachel Oliver.
Author:	Oliver, Rachel,
Description:	1 electronic resource (385 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
Contained By:	Dissertations Abstracts International86-03B.
Subject:	Aerospace engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31489364
ISBN:	9798384053699

An Event-Based Vision Sensor Simulation Framework for Space Domain Awareness Applications /
Oliver, Rachel,

An Event-Based Vision Sensor Simulation Framework for Space Domain Awareness Applications /Rachel Oliver. - 1 electronic resource (385 pages)

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

Event-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.

English

ISBN: 9798384053699Subjects--Topical Terms:

1002622
Aerospace engineering.
Subjects--Index Terms:

Event generation simulation

An Event-Based Vision Sensor Simulation Framework for Space Domain Awareness Applications /
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520 $a Event-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.
546 $a English
590 $a School code: 0058
650 4 $a Aerospace engineering. $3 1002622
650 4 $a Mechanical engineering. $3 649730
650 4 $a Astronomy. $3 517668
653 $a Event generation simulation
653 $a Event-based sensing
653 $a Neuromorphic sensors
653 $a Satellite tracking
653 $a Space Domain Awareness
653 $a Sparse data classification
690 $a 0538
690 $a 0548
690 $a 0606
710 2 $a Cornell University. $b Aerospace Engineering. $e degree granting institution. $3 3830014
720 1 $a Savransky, Dmitry $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-03B.
790 $a 0058
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31489364