Efficiently Identifying Close Track/Observation Pairs in Continuous Timed Data
by Jeremy Kubica, Andrew Moore, Andrew Connolly and Robert Jedicke
BibTeX:
@inproceedings{kubicaSPIE2005,
author = "Jeremy Kubica and Andrew Moore and Andrew Connolly and Robert Jedicke
title = "Efficiently Identifying Close Track/Observation Pairs in Continuous Timed Data",
booktitle = "Proc. SPIE Signal and Data Processing of Small Targets",
editor = "Oliver E. Drummond",
publisher = "SPIE",
month = "August",
year = "2005"
}
Abstract:
In this paper we examine new data structures and algorithms for
efficient and accurate gating and identification of potential
track/observation associations. Specifically, we focus on the problem of
continuous timed data, where observations arrive over a range of time
and each observation may have a unique time stamp. For example, the data
may be a continuous stream of observations or consist of many small
observed subregions. This contrasts with previous work in accelerating
this task, which largely assumes that observations can be treated as
arriving in batches at discrete time steps. We show that it is possible
to adapt established techniques to this modified task and introduce a
novel data structure for tractably dealing with very large sets of
tracks. Empirically we show that these data structures provide a
significant benefit in both decreased computational cost and increased
accuracy when contrasted with treating the observations as if they
occurred at discrete time steps.
Copyright 2005 Society of Photo-Optical Instrumentation Engineers. This paper was published in 2005 SPIE Conf. on Signal and Data Processing of Small Targets and is made available as an electronic reprint with permission of SPIE. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
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