TY - GEN
T1 - Reachability Analysis Based Tracking
T2 - 3rd International Conference on Dynamic Data Driven Application Systems, DDDAS 2020
AU - Hall, Zach
AU - Singla, Puneet
N1 - Funding Information:
This work is supported through AFOSR award #FA9550-17-1-0088.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - This paper presents a reachability set based method for tracking maneuvering space objects in the presence of sparse measurements. The proposed approach invokes the Dynamic Data Driven Application Systems (DDDAS) paradigm by dynamically integrating model forecasts due to uncertainties in maneuver capabilities with collected sensor data to search and track for a non-cooperative satellite in a control theoretic framework. The typically large time interval between measurements from ground stations presents significant problems for tracking satellites that have maneuvered during this interval. Using reachability set propagation techniques and a particle filter update scheme, an intelligently guided search algorithm is developed. This algorithm enables the systematic reduction of likely reachable states until measurements of the target are acquired and traditional tracking techniques can be resumed. Numerical simulations of a space-based sensor tasking scenario are given, however, the method is generic and can be extended to ground-based sensors or a combination of both ground and space-based sensors.
AB - This paper presents a reachability set based method for tracking maneuvering space objects in the presence of sparse measurements. The proposed approach invokes the Dynamic Data Driven Application Systems (DDDAS) paradigm by dynamically integrating model forecasts due to uncertainties in maneuver capabilities with collected sensor data to search and track for a non-cooperative satellite in a control theoretic framework. The typically large time interval between measurements from ground stations presents significant problems for tracking satellites that have maneuvered during this interval. Using reachability set propagation techniques and a particle filter update scheme, an intelligently guided search algorithm is developed. This algorithm enables the systematic reduction of likely reachable states until measurements of the target are acquired and traditional tracking techniques can be resumed. Numerical simulations of a space-based sensor tasking scenario are given, however, the method is generic and can be extended to ground-based sensors or a combination of both ground and space-based sensors.
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U2 - 10.1007/978-3-030-61725-7_24
DO - 10.1007/978-3-030-61725-7_24
M3 - Conference contribution
AN - SCOPUS:85097364971
SN - 9783030617240
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 200
EP - 207
BT - Dynamic Data Driven Application Systems - Third International Conference, DDDAS 2020, Proceedings
A2 - Darema, Frederica
A2 - Blasch, Erik
A2 - Ravela, Sai
A2 - Aved, Alex
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 2 October 2020 through 4 October 2020
ER -