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Proceedings Paper

An estimation of distribution method for infrared target detection based on Copulas
Author(s): Shuo Wang; Yiqun Zhang
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Paper Abstract

Track-before-detect (TBD) based target detection involves a hypothesis test of merit functions which measure each track as a possible target track. Its accuracy depends on the precision of the distribution of merit functions, which determines the threshold for a test. Generally, merit functions are regarded Gaussian, and on this basis the distribution is estimated, which is true for most methods such as the multiple hypothesis tracking (MHT). However, merit functions for some other methods such as the dynamic programming algorithm (DPA) are non-Guassian and cross-correlated. Since existing methods cannot reasonably measure the correlation, the exact distribution can hardly be estimated. If merit functions are assumed Guassian and independent, the error between an actual distribution and its approximation may occasionally over 30 percent, and is divergent by propagation. Hence, in this paper, we propose a novel estimation of distribution method based on Copulas, by which the distribution can be estimated precisely, where the error is less than 1 percent without propagation. Moreover, the estimation merely depends on the form of merit functions and the structure of a tracking algorithm, and is invariant to measurements. Thus, the distribution can be estimated in advance, greatly reducing the demand for real-time calculation of distribution functions.

Paper Details

Date Published: 15 October 2015
PDF: 9 pages
Proc. SPIE 9674, AOPC 2015: Optical and Optoelectronic Sensing and Imaging Technology, 96740S (15 October 2015); doi: 10.1117/12.2197805
Show Author Affiliations
Shuo Wang, China Aerospace Science and Industry Corp. (China)
Yiqun Zhang, China Aerospace Science and Industry Corp. (China)

Published in SPIE Proceedings Vol. 9674:
AOPC 2015: Optical and Optoelectronic Sensing and Imaging Technology
Haimei Gong; Nanjian Wu; Yang Ni; Weibiao Chen; Jin Lu, Editor(s)

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