AN APPROXIMATE MINIMUM MOSPA ESTIMATOR
Target Detection and Localisation
Přednášející: Peter Willett, Autoři: David Crouse, Peter Willett, University of Connecticut, United States; Marco Guerriero, Elt Elettronica S.p.A., Italy; Lennart Svensson, Chalmers University of Technology, Sweden
Optimizing over a variant of the Mean Optimal Subpattern Assignment (MOSPA) metric is equivalent to optimizing over the track accuracy statistic often used in target tracking benchmarks. Past work has shown how obtaining a Minimum MOSPA (MMOSPA) estimate for target locations from a Probability Density Function (PDF) outperforms more traditional methods (e.g. maximum likelihood (ML) or Minimum Mean Squared Error (MMSE) estimates) with regard to track accuracy metrics. In this paper, we derive an approximation to the MMOSPA estimator in the two-target case, which is generally very complicated, based on minimizing a Bhattacharyya-like bound. It has a particularly nice form for Gaussian mixtures. We thence compare the new estimator to that obtained from using the MMSE and the optimal MMOSPA estimators.
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- AN APPROXIMATE MINIMUM MOSPA ESTIMATOR [PDF], 1.04 MB
Informace o přednášce
| Nahráno: | 2011-05-25 10:30 - 10:50, Club B |
|---|---|
| Přidáno: | 19. 6. 2011 19:09 |
| Počet zhlédnutí: | 31 |
| Rozlišení videa: | 1024x576 px, 512x288 px |
| Délka videa: | 0:18:47 |
| Audio stopa: | MP3 [6.34 MB], 0:18:47 |
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