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Space Systems Design Laboratory (SSDL)

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Has files: Yes × Author: Holzinger, Marcus J. × Author: Flewelling, Brien R. ×

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Now showing 1 - 4 of 4
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    Direct Image-to-Likelihood for Track-Before-Detect Multi-Bernoulli Filter
    (Georgia Institute of Technology, 2016-02) Murphy, Timothy S. ; Holzinger, Marcus J. ; Flewelling, Brien R.
    This paper aims to apply the random finite set-based multi-Bernoilli filter to frame to- frame tracking of space objects observed in electro optical imagery for space domain awareness applications. First, this paper will review random finite set filters applied to frame to frame tracking and their applications to space. A new likelihood function for space based imagery will be presented, based on the matched filter. A more educated birth model will be proposed which better models potential SO using observer characteristics and object dynamics. Simulation results will explore the range of objects that can be tracked. The final algorithm is able to perform completely uncued detection down to a total object SNR of 5.6 and a per pixel SNR of 1.5. Promising but inconclusive results are shown for total object SNR of 3.35 and per pixel SNR of 0.7.
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    Spatio-Temporal Scale Space Analysis of Photometric Signals with Tracking Error
    (Georgia Institute of Technology, 2015-09) Flewelling, Brien R. ; Murphy, Timothy S. ; Rhodes, Andrew P. ; Holzinger, Marcus J. ; Christian, John A.
    This paper will investigate the application of Scale-Space Theory, specifically Curvature Scale Space, to 1-Dimensional light curve signals generated by reducing imagery sequences taken from simulated telescopes tasked in various modes. As an observed object with a variable light curve is viewed from a sensor achieving a perfect rate track mode, there is a trade between the time fidelity of the reconstructed signal and integration time required to make accurate detections. As the tracking error increases, a sensor in a step-stare con-ops for example may trade spatial samples for intensity information as a function of time. This is commonly seen in streak observations of tumbling resident space objects. The method presented here will demonstrate how consistent light curves with maximum time resolution can be generated from observation sequences with variable tracking error, and sensor integration times. Additionally, the sparse representation of these signals using Curvature Scale-Space feature images will be investigated as a means for rapid correlation of light-curves against a large database. The proposed rapid correlations could be used to identify variable operating modes of a known object, or to identify an object as a member of a database using a method dependent on the order of the number of salient features as opposed to the number of observations.
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    Orbit Determination for Partially Understood Object via Matched Filter Bank
    (Georgia Institute of Technology, 2015-08) Murphy, Timothy S. ; Holzinger, Marcus J. ; Flewelling, Brien R.
    With knowledge of a space object's orbit, the matched filter is an image processing technique which allows low signal-to-noise ratio objects to be detected. Many space situational awareness research efforts have looked at ways to characterize the probability density function of a partially understood space object. When prior knowledge is only constrained to a probability density function, many matched filter templates could be representative of the space object, necessitating a bank of matched filters. This paper develops the measurement dissimilarity metric which is then applied to partition a general prior set of orbits. A method for hypothesis testing the result of a matched filter for a space object is developed. Finally, a framework for orbit determination based on the matched filter result is developed. Simulation shows that the analytic results enable more efficient computation and a better framework for implementing matched filters.
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    Enabling Direct Feedback Between Initial Orbit Determination and Sensor Data Processing for Detection and Tracking of Space Objects
    (Georgia Institute of Technology, 2015-04) Sease, Brad ; Murphy, Timothy S. ; Flewelling, Brien R. ; Holzinger, Marcus J. ; Black, Jonathan
    This paper presents an automatic RSO detection and tracking scheme operating at the optical sensor system level. The software presented is a pipeline for processing ground or space-based imagery built from several sub-algorithms which processes raw or calibrated imagery, detects and discriminates non-star objects, and associates observations over time. An orbit determination routine uses an admissible region to start off an unscented particle filter. This preliminary orbit estimate allows prediction of the appearance of the object in the next frame. A matched filter uses this imagery to provide feedback to the initial detection and tracking process.