Efficiently identifying close track/observation pairs in continuous timed data

Jeremy Kubica; Andrew Moore; Andrew Connolly; Robert Jedicke

doi:10.1117/12.617607

15 September 2005 Efficiently identifying close track/observation pairs in continuous timed data

Jeremy Kubica, Andrew Moore, Andrew Connolly, Robert Jedicke

Proceedings Volume 5913, Signal and Data Processing of Small Targets 2005; 59130S (2005) https://doi.org/10.1117/12.617607
Event: Optics and Photonics 2005, 2005, San Diego, California, United States

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.

Citation Download Citation

Jeremy Kubica, Andrew Moore, Andrew Connolly, and Robert Jedicke "Efficiently identifying close track/observation pairs in continuous timed data", Proc. SPIE 5913, Signal and Data Processing of Small Targets 2005, 59130S (15 September 2005); https://doi.org/10.1117/12.617607

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