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Daniel Guggenheim School of Aerospace Engineering

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Author: Holzinger, Marcus J. × Author: Virani, Shahzad × End date: 2017 × Start date: 2017 × Item Type: Publication × search.filters.applied.f.isOrgUnitOfPublicationTitle: Space Systems Design Laboratory (SSDL) ×

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Real-Time Hardware-in-the-Loop Hand-Off from a Finder Scope to a Larger Telescope

2017-11 , Aguilar-Marsillach, Daniel , Virani, Shahzad , Holzinger, Marcus J.

Electro-optical sensors play an increasingly important role in the SSA domain for tracking satellites and debris objects. Such sensors provide data that complement other methods, like radar based sensing, by providing a higher angular resolution, and thus improving the estimation of an object’s orbit, attitude and physical properties. The acquisition of such data is invaluable for obtaining more accurate collision risk assessments and formulating improved debris mitigation efforts. The Georgia Tech - Space Object Research Telescope aims to improve detection and tracking for agile Raven-class telescopes with narrow fields of view and high angular resolutions. A secondary imaging system was used to correct the Georgia Tech - Space Object Research Telescope’s pointing errors for tracking objects at high angular rates using a closed-loop controller. This paper will focus on the development and results of a real-time hardware-in-the-loop hand-off from a finder scope to a larger telescope.

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Empirical Dynamic Data Driven Detection Tracking Using Detectionless and Traditional FiSSt Methods

2017-09 , Virani, Shahzad , Murphy, Timothy S. , Holzinger, Marcus J. , Jones, Brandon A.

Autonomous search and recovery of resident space object (RSO) tracks is crucial for decision makers in SSA. This paper leverages dynamic data driven approaches to improve methodologies used in real-time detection and tracking of RSOs with a low signal-to-noise ratio (SNR). Detected RSOs are assigned to be tracked using one of two simultaneously operating algorithms. The Gaussian Mixture Proability Hypothesis Density (GM-PHD) filter tracks all RSOs above a certain SNR threshold, while a Detectionless Multi-Bernoulli filter (D-MB) detects and tracks low SNR objects. The D-MB filter uses matched filtering for likelihood computation which is highly non-Gaussian for dim objects. Hence, the D-MB filter is particle based which leads to higher computational complexity. The primary idea proposed in this paper is to balance the computational efficiency of GM-PHD and high sensitivity of the D-MB likelihood computation by dynamically switching tracks between the two filters based on the SNR of the target; allowing for real-time detection and tracking. These algorithms are implemented and tested on real data of objects in the geostationary (GEO) belt using a wide field-of-view camera (18.2 degrees). A star tracking mount is used to inertially stare at the GEO belt and data are collected for 2 hours corresponding to RSOs being observed in 48.2 degrees of the GEO belt.

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