Event-based Sensing for Space Situational Awareness

Cohen, Gregory; Afshar, Saeed; Morreale, B.; Bessell, Travis; Wabnitz, Andrew; Rutten, Mark; Schaik, André van

doi:10.1007/s40295-018-00140-5

Cited by 63 publications

(51 citation statements)

References 18 publications

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“…Defence Science and Technology (DST) Group have been directly involved in SSA research projects (Ash et al, 2018), including operation of an optical space telescope for use during solar terminator periods (Hobson et al, 2016). The use of nontraditional space sensors has also been investigated, such as event-based sensors (Cohen et al, 2017), an experimental HF line-of-sight (LOS) radar system (Frazer et al, 2013(Frazer et al, , 2014, and the Murchison Widefield radio astronomy array (MWA) (Tingay et al, 2013) for passive radar observations of satellite reflections from FM transmitters of opportunity (Palmer et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Low Earth Orbit Object Observations Using the Buckland Park VHF Radar

2020

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There is increasing interest in space situational awareness worldwide, motivating investigation of the use of nontraditional sensors for space surveillance. This paper presents preliminary results investigating the use of a VHF wind profiling radar for observing objects in low Earth orbit. This radar class is low cost relative to other radars typically applied to this task. The results reveal that 2,410 objects were detected over 15 days, with 1,392 unique objects detected. The daily detection count rates ranged from 150 to 200, and the maximum detection height observed was 2,491 km. The radar's utility for object catalog maintenance is demonstrated by its ability to determine propagation state vector errors, and through observations of the Chinese space station Tiangong‐1 in the last months of its return to Earth. The results suggest the measurements may be able to provide useful ionospheric parameters such as total electron content (TEC) measurements, provided high precision ephemeris data are available for the detected objects.

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Section: Introductionmentioning

confidence: 99%

Low Earth Orbit Object Observations Using the Buckland Park VHF Radar

2020

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“…One recent example of the use of event-based sensors in a real-world application is in the field of event-based space situational awareness [46], where the inherent wide dynamic range of event-based sensors enables detection of non-terrestrial targets both at night and during daylight. This unique advantage over traditional CCD sensors that saturate during daylight hours, together with the drastically lower data rates resulting from event-based sensing of the dark empty background of the space environment, makes event-based sensing uniquely suitable for space situational awareness.…”

Section: B Plane Dropping Dataset Resultsmentioning

confidence: 99%

Event-Based Feature Extraction Using Adaptive Selection Thresholds

Afshar

Ralph

et al. 2020

Sensors

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Unsupervised feature extraction algorithms form one of the most important building blocks in machine learning systems. These algorithms are often adapted to the event-based domain to perform online learning in neuromorphic hardware. However, not designed for the purpose, such algorithms typically require significant simplification during implementation to meet hardware constraints, creating trade offs with performance. Furthermore, conventional feature extraction algorithms are not designed to generate useful intermediary signals which are valuable only in the context of neuromorphic hardware limitations. In this work a novel event-based feature extraction method is proposed that focuses on these issues. The algorithm operates via simple adaptive selection thresholds which allow a simpler implementation of network homeostasis than previous works by trading off a small amount of information loss in the form of missed events that fall outside the selection thresholds. The behavior of the selection thresholds and the output of the network as a whole are shown to provide uniquely useful signals indicating network weight convergence without the need to access network weights. A novel heuristic method for network size selection is proposed which makes use of noise events and their feature representations. The use of selection thresholds is shown to produce network activation patterns that predict classification accuracy allowing rapid evaluation and optimization of system parameters without the need to run back-end classifiers.The feature extraction method is tested on both the N-MNIST benchmarking dataset and a dataset of airplanes passing through the field of view. Multiple configurations with different classifiers are tested with the results quantifying the resultant performance gains at each processing stage.

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“…These aspects include the sparse pixel subset associated with targets in the DVS output, atmospheric effects, and platform jitter, among other detriments. While other works have successfully demonstrated DVS capabilities in topics such as target recognition and tracking 8,9,10 , much work remains in researching DVS behavior. It should also be noted that, while the DVS model is derived and validated based on real data, simulations by themselves are not enough to prove the spatio-temporal advantage of the DVS.…”

Section: Discussionmentioning

confidence: 99%

Tracking from a moving platform with the Dynamic Vision Sensor

Cox

Morley

2019

Computational Imaging IV

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The Dynamic Vision Sensor (DVS) is an imaging sensor that processes the incident irradiance image and outputs temporal log irradiance changes in the image, such as those generated by moving target(s) and/or the moving sensor platform. From a static platform, this enables the DVS to cancel out background clutter and greatly decrease the sensor bandwidth required to track temporal changes in a scene. However, the sensor bandwidth advantage is lost when imaging a scene from a moving platform due to platform motion causing optical flow in the background. Imaging from a moving platform has been utilized in many recently reported applications of this sensor. However, this approach inherently outputs background clutter generated from optical flow, and as such this approach has limited spatio-temporal resolution and is of limited utility for target tracking applications. In this work we present a new approach to moving target tracking applications with the DVS. Essentially, we propose modifying the incident image to cancel out optical flow due to platform motion, thereby removing background clutter and recovering the bandwidth performance advantage of the DVS. We propose that such improved performance can be accomplished by integrating a hardware tracking and stabilization subsystem with the DVS. Representative simulation scenarios are used to quantify the performance of the proposed approach to clutter cancellation and improved sensor bandwidth.

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Event-based Sensing for Space Situational Awareness

Cited by 63 publications

References 18 publications

Low Earth Orbit Object Observations Using the Buckland Park VHF Radar

Low Earth Orbit Object Observations Using the Buckland Park VHF Radar

Event-Based Feature Extraction Using Adaptive Selection Thresholds

Tracking from a moving platform with the Dynamic Vision Sensor

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