Title :
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
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;
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
DOI :
10.1109/APCCAS.2000.913517
