2017 IEEE International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET 2017)

242 / 2017-01-16 05:30:28

Doppler-shift invariant feature extraction for underwater acoustic target classification

12524,12523,5967,12522

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 spectrum features when target move in a highly maneuverable manner. The signal is firstly filtered by a set of quasi-orthogonal triangle filters into its frequency domain with a linear property in the log scale. Then the time-variant Doppler shift for each prominent line component due to the maneuverability tends to be a frequency-invariant offset, which can be easily removed. Such a Doppler-shift invariant spectrum feature removes the irrelevant Doppler-shift caused by the change of the velocity 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.