TY - GEN
T1 - Doppler-shift invariant feature extraction for underwater acoustic target classification
AU - Wang, Lu
AU - Wang, Qiang
AU - Zhao, Lifan
AU - Zeng, Xiangyang
AU - Bi, Guoan
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Spectrum is one of the commonly used but effective feature for underwater acoustic target classification. However, features extracted based on the spectra are vulnerable to the change of acoustic channels. We propose in this paper a Doppler-shift invariant spectrum feature extraction method which can extract inherent and stable features when target moves in a highly maneuverable manner. The signal is firstly filtered by a set of quasi-orthogonal triangular filters into its frequency domain with a linear property in the log scale. Then the time-variant Doppler shift for each prominent line component tends to be a frequency-invariant offset, which can be easily removed. Such a Doppler-shift invariant feature removes the irrelevant target's motion information and separates out the inherent target spectrum feature. Numerical real data results demonstrate that the proposed feature outperforms the traditional ones in underwater target classification.
AB - Spectrum is one of the commonly used but effective feature for underwater acoustic target classification. However, features extracted based on the spectra are vulnerable to the change of acoustic channels. We propose in this paper a Doppler-shift invariant spectrum feature extraction method which can extract inherent and stable features when target moves in a highly maneuverable manner. The signal is firstly filtered by a set of quasi-orthogonal triangular filters into its frequency domain with a linear property in the log scale. Then the time-variant Doppler shift for each prominent line component tends to be a frequency-invariant offset, which can be easily removed. Such a Doppler-shift invariant feature removes the irrelevant target's motion information and separates out the inherent target spectrum feature. Numerical real data results demonstrate that the proposed feature outperforms the traditional ones in underwater target classification.
KW - Doppler-invariant feature
KW - spectrum
KW - the mel-frequency cepstral coefficients
KW - Underwater targets classification
UR - http://www.scopus.com/inward/record.url?scp=85046353007&partnerID=8YFLogxK
U2 - 10.1109/WiSPNET.2017.8299955
DO - 10.1109/WiSPNET.2017.8299955
M3 - 会议稿件
AN - SCOPUS:85046353007
T3 - Proceedings of the 2017 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2017
SP - 1209
EP - 1212
BT - Proceedings of the 2017 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2017
Y2 - 22 March 2017 through 24 March 2017
ER -