Underwater Target Classification Using Wavelet Packets and Neural Neteorks  

Oleh:

Dobeck, G. J. ; Azimi-Sadjadi, M. R. ; Yao, De ; Huang, Qiang

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 11 no. 3 (2000), page 784-794.
Topik: Wavelet; underwater; target classification; underwater; classification; wavelet packets; neural networks

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.4 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

In this paper, a new subband - based classification scheme is developed for classifying underwater mines and mine - like targets from the acoustic backscattered signals. The system consists of a feature extractor using wavelet packets in conjunction with linear predictive coding (LPC), a feature selection scheme, and a backpropagation neural - network classifier. The data set used for this study consists of the backscattered signals from six different objects : two mine - like targets and four nontargets for several aspect angles. Simulation results on ten different noisy realizations and for signal-to-noise ratio (SNR) of 12 dB are presented. The receiver operating characteristic (ROC) curve of the classifier generated based on these results demonstrated excellent classification performance of the system. The generalization ability of the trained network was demonstrated by computing the error and classification rate statistics on a large data set. A multi aspect fusion scheme was also adopted in order to further improve the classification performance.

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