Application of RELAX in Line Spectrum Estimation

Wu, Renbiao; Jia, Qiongqiong; Yang, Lei; Feng, Qu

doi:10.1007/978-981-13-6932-2_3

Cited by 1 publication

(1 citation statement)

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“…In [1], a 9-dimensional feature vector is constructed, containing zero-crossing wavelength, peak-to-peak amplitude, zerocrossing wavelength difference, and wave train area, which achieves a recognition rate of 89.5% on the test data, through a support vector machine (SVM) with a radial basis function (RBF) as the kernel function. In [2], the line spectrum feature and the average power spectrum feature of the underwater acoustic target radiated noise is extracted by using the Fourier transform. In [3], a high-order cumulant feature extraction method is proposed based on the Hilbert-Huang transform, which first performs the Hilbert-Huang transform on the target signal, and then extracts high-order cumulant features from the obtained intrinsic mode functions.…”

Section: Introductionmentioning

confidence: 99%

Research on Feature Extraction of Underwater Acoustic Target Radiation Noise Based on Machine Learning Algorithm

Liu,

Han,

Zhang

et al. 2023

J. Phys.: Conf. Ser.

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Underwater acoustic target recognition is a very important technology in the field of underwater acoustics, with great economic and military value. Feature extraction technology for underwater acoustic target radiation noise signals is the key to achieving acoustic target recognition. This study aims at the feature extraction task of acoustic targets and extracts 10 types of 252-dimensional feature vectors from three domains: time domain, frequency domain, and auditory domain. Through 7 machine learning algorithms for classification and recognition experiments, the experimental results show that the recognition performance of the ensemble classifier is much better than that of a single classifier. For different types of features, this study combines three ensemble learning algorithms and feature selection algorithms to select the original 252-dimensional features. The feature selection experiment shows that the wrapper feature selection algorithm has the best effect, and the feature vector dimension can be reduced to 40 dimensions. The recognition accuracy rate is not less than 92.8%, which provides feature extraction guidance for acoustic target recognition based on feature extraction.

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Section: Introductionmentioning

confidence: 99%