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Proceedings Paper

Object aggregation using Neyman-Pearson analysis
Author(s): Li Bai; Michael L. Hinman
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Paper Abstract

This paper presents a novel approach to: 1) distinguish military vehicle groups, and 2) identify names of military vehicle convoys in the level-2 fusion process. The data is generated from a generic Ground Moving Target Indication (GMTI) simulator that utilizes Matlab and Microsoft Access. This data is processed to identify the convoys and number of vehicles in the convoy, using the minimum timed distance variance (MTDV) measurement. Once the vehicle groups are formed, convoy association is done using hypothesis techniques based upon Neyman Pearson (NP) criterion. One characteristic of NP is the low error probability when a-priori information is unknown. The NP approach was demonstrated with this advantage over a Bayesian technique.

Paper Details

Date Published: 1 April 2003
PDF: 10 pages
Proc. SPIE 5099, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2003, (1 April 2003); doi: 10.1117/12.487056
Show Author Affiliations
Li Bai, Temple Univ. (United States)
Michael L. Hinman, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 5099:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2003
Belur V. Dasarathy, Editor(s)

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