A comparison of neural networks and statistical methods for track association in over the horizon radar

Author

Zhu, J. ; Bogner, R.E. ; Bouzerdoum, A. ; Pope, K.J. ; Southcott, M.L.

Author_Institution

Dept. of Electr. & Electron. Eng., Adelaide Univ., SA, Australia

Volume

5

fYear

1995

fDate

Nov/Dec 1995

Firstpage

2415

Abstract

An ionospheric model-free pattern classification approach is proposed for associating tracks in over the horizon radar. A set of track features and track affinity measures are derived according to human perceptual grouping principles. To facilitate the pairwise association of the tracks, neural networks and statistical methods are applied to combine different track affinities. A posterior pseudo-probability measuring association is produced for every pair of tracks

Keywords

feature extraction; multilayer perceptrons; pattern classification; radar target recognition; statistical analysis; human perceptual grouping principles; ionospheric model-free pattern classification; neural networks; over the horizon radar; pairwise association; statistical methods; track affinities; track association; Feature extraction; Humans; Intelligent networks; Ionosphere; Neural networks; Pattern classification; Radar tracking; Statistical analysis; Target tracking; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1995. Proceedings., IEEE International Conference on

Conference_Location

Perth, WA

Print_ISBN

0-7803-2768-3

Type

conf

DOI

10.1109/ICNN.1995.487740

Filename

487740