Doctor of Philosophy Proposal Assignment

Doctor of Philosophy Proposal
Title: Event-Based Imaging for Space Situation Awareness
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RESEARCH BACKGROUND
The number of satellites has currently upsurged, and due to their increased velocity;
unprecedented collisions can occur such as Collison with another satellite, collision with
space junk, unexpected accidents, as well as hostile action for the resultant debris cloud. The
implication will likely degenerate into destroying satellites and spacecraft [1]. As a result,
one concept; Space Situational awareness (SSA) has been developed to address the
underlying challenges. SSA focus on initiating space traffic management STM. Most of the
satellites, which have been launched over the last 50 years, are characterised with
incapacitating anomalies and space debris. To evade the collisions, SAA is aiming at
predicting the physical location of natural and artificial items in the orbit to evade collisions.
Mahowald et al [2] designed a spake based - based neuromorphic camera with an Address
Event Representation (AER) vision system. It is a unique biological system since not even
the traditional CCD and humans (asynchronous events act on the sensing systems of
organisms, and the information is processed hierarchically and in parallel in a massive neuron
network) works similarly. SSA precisely adopts the neuromorphic sensors, chips as well as
Deep Learning approach to address the issue.
Event-based cameras are bio-inspired sensors which respond to brightness changes
asynchronously and independently for each pixel, thus offering several advantages over the
conventional cameras. For instance low latency, low power, high speed and high dynamic
range (HDR). Since their emergence, various applications for computer vision and robotics
such as visual tracking, detection and recognition, Simultaneous Localisation and Mapping
(SLAM), Visual Reconstruction, and Stereo Matching have been proposed.
Research Questions
In relation to the idea, below are some questions, which will offer guidance to the research:
 can event-based cameras with silicon retinas feature detect/track satellites?
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 How can Adaptive Optics (AO) system be useful to improve the detecting and
tracking tasks?
 Can Deep Learning techniques and algorithms be used to identify the shape of space
junk in computer vision?
 Can the range be calculated through the cooperation between multiple telescopes and
event-based cameras?
 Will forecasting image sensors characterised by variable spatial resolution across the
surface of the sensor target data reduction without a critical impact to the final
execution of the application?
KNOWLEDGE GAPS
Despite the impressive technological development in various fields, the past research has not
provided a reliable and robust extraordinary algorithm and application in SSA, specifically in
tracking and detecting of satellites and space junks. Tracking is of pivotal importance in
supervision applications, and due to the nature of objects, it becomes complex to extract
datasets from such scenes [3].
Tracking is limited for objects with hyper velocity, especially when the standard camera is
used. These cameras lack the Data Fusion (DF) process of integrating various data sources
from multiple event-based cameras and other devices. Besides, there are many objects in
space around earth, which makes it difficult to detect and track from the ground through these
noises in space. Correspondingly, sending a piece of equipment to space is exorbitant, as it is
faced with challenges of adaptations and possible collisions with other space objects.
Therefore, limited research has been conducted on the use of Bio-inspired Sensors and Data
Fusion in capturing space objects through the ground truth model.
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