Open AccessEffect of Multichannel Signal Sequence on Source Localization Using Convolutional Neural Network
Author(s) -
Yinquan Zhang,
Shuang Zhang,
Kaiming Wu,
Siyu Gao,
Dong Li,
Jie Liu,
Guofu Li
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/677/5/052040
Subject(s) - convolutional neural network , signal (programming language) , computer science , sequence (biology) , range (aeronautics) , artificial intelligence , pattern recognition (psychology) , fourier transform , artificial neural network , algorithm , speech recognition , mathematics , engineering , mathematical analysis , genetics , biology , programming language , aerospace engineering
In recent years, machine learning has become a promising data-driven method of source localization in underwater acoustics. Several algorithms have been developed by taking advantage of neural networks. This paper investigates the effect of multichannel signal sequence on the performance of source localization using a convolutional neutral network (CNN). In this paper, source localization is solved as a classification problem. The performances of different sequences are demonstrated to be quite different. For a specific CNN, it is revealed that the multichannel sequence affects source localization through influencing the complexity of range classification. The complexity can be reasonably reflected by the conspicuousness of signal differences between adjacent range categories. The two-dimensional (2D) Fourier spectrum of the signal differences provides an intuitive approach to describe the conspicuousness. The multichannel sequence that could induce greater spectral amplitudes has better localization performance in noisy environments.