Title:
Empirical Dynamic Data Driven Detection Tracking Using Detectionless and Traditional FiSSt Methods
Empirical Dynamic Data Driven Detection Tracking Using Detectionless and Traditional FiSSt Methods
Author(s)
Virani, Shahzad
Murphy, Timothy S.
Holzinger, Marcus J.
Jones, Brandon A.
Murphy, Timothy S.
Holzinger, Marcus J.
Jones, Brandon A.
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Abstract
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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Date Issued
2017-09
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