A Feature Extraction Algorithm of Underwater Acoustic Target Based on LOFAR Distribution
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DOI: 10.23977/cnci2021.012
Author(s)
Xinliang Li, Bo Yu, Gongliang Hu and Chunyu Zhang
Corresponding Author
Gongliang Hu
ABSTRACT
As one of primary analysis approaches of passive sonar signals, LOFAR (Low
Frequency Analysis and Recording) spectrum has been used in underwater acoustic target
detection, tracking, classification, etc. However, spectral components of underwater
acoustic signals are complex, because the signals may be affected by sea features and noise
radiated from targets. As a consequence, the passive sonar signals are not stationarity, and
the result of detection with LOFAR spectrum is not ideal, against background noise. Therefore, this paper proposes an algorithm of feature extraction based on LOFAR spectral
distribution (FELSD), in order to improve the performance of underwater acoustic target on
classification. The proposed algorithm is evaluated in a real dataset collected by a passive
sonar system that has been installed in a submarine, and experiments show that our
algorithm has higher accuracy of classification (supervised and unsupervised), compared
with traditional methods.
KEYWORDS
Hydroacoustic engineering, lofar, spectral distribution, feature extraction