Abstract
Multitarget terrain-based tracking is a cyclic process that combines sensor information with state estimation and data association techniques to maintain an estimate of the state of an environment in which ground-based vehicles are operating. When the ground-based vehicles are military vehicles moving across terrain, most of them will being moving in groups instead of autonomously. This work presents a methodology that has been demonstrated to improve the estimation aspect of the tracking process for this military domain. A clustering algorithm identifies groups within a vehicular data set. Group characteristics are extracted and then a new, innovative technique is utilized to integrate these into the individual vehicles’ state estimation process. A series of experiments shows that the proposed methodology significantly improves the performance of three classic estimation algorithms for multitarget terrain-based tracking.
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Sobiesk, E., Gini, M., Marin, J.A. (2007). Using Group Knowledge for Multitarget Terrain-Based State Estimation. In: Alami, R., Chatila, R., Asama, H. (eds) Distributed Autonomous Robotic Systems 6. Springer, Tokyo. https://doi.org/10.1007/978-4-431-35873-2_12
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DOI: https://doi.org/10.1007/978-4-431-35873-2_12
Publisher Name: Springer, Tokyo
Print ISBN: 978-4-431-35869-5
Online ISBN: 978-4-431-35873-2
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