Analysis of target classification performances of active sonar returns depending on parameter values of SVM kernel functions

Park, Jeonghyun; Hwang, Chan-Sik; Bae, Kyung‐Yeol

doi:10.6109/jkiice.2013.17.5.1083

Cited by 2 publications

(3 citation statements)

References 5 publications

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“…Recent research on SSS target recognition has shown that the target recognition method based on CNNs has outperformed traditional machine learning methods, including the fuzzy logic method, K nearest neighbor, support vector machines, etc., [15][16][17]. With an increasing scale, the recognition network can extract deeper features from images to obtain richer feature information.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Mode Channel Position Attention Fusion Side-Scan Sonar Transfer Recognition

Wang

Huo

et al. 2023

Electronics

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Side-scan sonar (SSS) target recognition is an important part of building an underwater detection system and ensuring a high-precision perception of underwater information. In this paper, a novel multi-channel multi-location attention mechanism is proposed for a multi-modal phased transfer side-scan sonar target recognition model. Optical images from the ImageNet database, synthetic aperture radar (SAR) images and SSS images are used as the training datasets. The backbone network for feature extraction is transferred and learned by a staged transfer learning method. The head network used to predict the type of target extracts the attention features of SSS through a multi-channel and multi-position attention mechanism, and subsequently performs target recognition. The proposed model is tested on the SSS test dataset and evaluated using several metrics, and compared with different recognition algorithms as well. The results show that the model has better recognition accuracy and robustness for SSS targets.

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Section: Literature Reviewmentioning

confidence: 99%

Multi-Mode Channel Position Attention Fusion Side-Scan Sonar Transfer Recognition

Wang

Huo

et al. 2023

Electronics

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“…In addition, SVM was less affected by the scattered and mixed characteristics of the underwater environment. A study on sonar data classification according to the kernel function of SVM [23] was performed. In [23], the results showed that the radial basis function (RBF) kernel had better performance than polynomial kernel.…”

Section: Introduction To the Support Vector Machinementioning

confidence: 99%

“…A study on sonar data classification according to the kernel function of SVM [23] was performed. In [23], the results showed that the radial basis function (RBF) kernel had better performance than polynomial kernel. For these reasons, we use a SVM classifier and a Gaussian function type RBF kernel, which has already been widely used for sonar classification.…”

Section: Introduction To the Support Vector Machinementioning

confidence: 99%

Underwater Moving Target Classification Using Multilayer Processing of Active Sonar System

et al. 2019

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The task of detecting and classifying highly maneuverable and unidentified underwater targets in complex environments is significant in active sonar systems. Previous studies have applied many detection schemes to this task using signals above a preset threshold to separate targets from clutter; this is because a high signal-to-noise ratio (SNR) target has sufficient feature vector components to be separated out. However, in real environments, the received target return’s SNR is not always above the threshold. Therefore, a target detection algorithm is needed for varied target SNR conditions. When the clutter energy is too strong, false detection can occur, and the probability of detection is reduced due to the weak target signature. Furthermore, since a long pulse repetition interval is used for long-range detection and ambient noise tends to be high, classification processing for each ping is needed. This paper proposes a multilayer classification algorithm applicable to all signals in real underwater environments above the noise level without thresholding and verifies the algorithm’s classification performance. We obtained a variety of experimental data by using a real underwater target and a hull-mounted active sonar system operated on Korean naval ships in the East Sea, Korea. The detection performance of the proposed algorithm was evaluated in terms of the classification rate and false alarm rate as a function of the SNR. Since experimental environment data, including the sea state, target maneuvering patterns, and sound speed, were available, we selected 1123 instances of ping data from the target over all experiments and randomly selected 1000 clutters based on the distribution of clutters for each ping. A support vector machine was employed as the classifier, and 80% of the data were selected for training, leaving the remaining data for testing. This process was carried out 1000 times. For the performance analysis and discussions, samples of scatter diagrams and feature characteristics are shown and classification tables and receiver operation characteristic (ROC) curves are presented. The results show that the proposed algorithm is effective under a variety of target strengths and ambient noise levels.

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Analysis of target classification performances of active sonar returns depending on parameter values of SVM kernel functions

Cited by 2 publications

References 5 publications

Multi-Mode Channel Position Attention Fusion Side-Scan Sonar Transfer Recognition

Multi-Mode Channel Position Attention Fusion Side-Scan Sonar Transfer Recognition

Underwater Moving Target Classification Using Multilayer Processing of Active Sonar System

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