Vehicle identification based on self-organizing artificial neural network

Author

Zhu, Jinan ; Hou, Yao ; Nie, Weirong

Author_Institution

Sch. of Mech. Eng., Nanjing Univ. of Sci. & Technol., China

fYear

2000

fDate

2000

Firstpage

391

Lastpage

394

Abstract

UGS (Unattended Ground System) has been proved to be effective in target real-defection, identification. This paper discusses data acquisition by seismic sensors and how to extract features from the seismic signal produced by a vehicle. ANN (artificial neural network) is a robust classifier in pattern recognition, especially the property of ART ANN self-learning is very suitable to target emergence in the battlefield, therefore this paper presents a method of target identification by ART ANN. A satisfactory experiment result is obtained after simulation

Keywords

ART neural nets; data acquisition; feature extraction; identification technology; military computing; pattern classification; seismic waves; vehicles; ART ANN self-learning; battlefield; data acquisition; pattern recognition; robust classifier; seismic sensors; seismic signal feature extraction; self-organizing ANN; self-organizing artificial neural network; target identification; unattended ground system; vehicle identification; Acoustic sensors; Artificial neural networks; Chemical sensors; Earth; Magnetic sensors; Neural networks; Seismic waves; Sensor systems; Surface waves; Vehicles;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 2000. IEEE APCCAS 2000. The 2000 IEEE Asia-Pacific Conference on

Conference_Location

Tianjin

Print_ISBN

0-7803-6253-5

Type

conf

DOI

10.1109/APCCAS.2000.913517

Filename

913517