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Daniel Guggenheim School of Aerospace Engineering

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Author: Murphy, Timothy S. × End date: 2017 × Start date: 2017 × Item Type: Publication × search.filters.applied.f.isOrgUnitOfPublicationTitle: Space Systems Design Laboratory (SSDL) ×

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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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Generalized Minimum-Time Follow-up Approaches Applied to Tasking Electro-Optical Sensor Tasking

2017-09 , Murphy, Timothy S. , Holzinger, Marcus J.

This work proposes a methodology for tasking of sensors to search an area of state space for a particular object, group of objects, or class of objects. This work creates a general unified mathematical framework for analyzing reacquisition, search, scheduling, and custody operations. In particular, this work looks at searching for unknown space object(s) with prior knowledge in the form of a set, which can be defined via an uncorrelated track, region of state space, or a variety of other methods. The follow-up tasking can occur from a variable location and time, which often requires searching a large region of the sky. This work analyzes the area of a search region over time to inform a time optimal search method. Simulation work looks at analyzing search regions relative to a particular sensor, and testing a tasking algorithm to search through the region. The tasking algorithm is also validated on a reacquisition problem with a telescope system at Georgia Tech.

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Optical Sensor Follow-up Tasking on High Priority Uncorrelated Track

2017-02 , Murphy, Timothy S. , Luu, K. Kim , Sabol, Chris , Holzinger, Marcus J..

This work proposes a methodology for tasking of electro-optical sensors to search an area of state space for a particular object. This work enables current space situational awareness programs to more efficiently follow-up on an unknown object. In particular, this work looks at searching for an unknown space object with prior knowledge in the form of a set, which can be defined via an uncorrelated track. The follow-up can occur from a different location at a different time, which often requires searching a large region of the sky. This work analyzes the divergence of a search region to inform a time optimal search method. Simulation work is included to explore the effects of sensor geometry, initial detection uncertainty, and handoff delay time on total time and feasibility of follow-up.

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