scholarly journals Joint Passive Detection and Tracking of Underwater Acoustic Target by Beamforming-Based Bernoulli Filter with Multiple Arrays

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4022 ◽  
Author(s):  
Zhongyue Chen ◽  
Wen Xu
Keyword(s):  
Hybrid Method ◽  
Pearson Correlation ◽  
Tracking Accuracy ◽  
Underwater Acoustic ◽  
Passive Detection ◽  
Detection And Tracking ◽  
Target Track ◽  
Distributed Arrays ◽  
Acoustic Target ◽  
Bernoulli Filter

In this paper, improved Bernoulli filtering methods are developed to deal with the problem of joint passive detection and tracking of an underwater acoustic target with multiple arrays. Three different likelihood calculation methods based on local beamforming results are proposed for the Bernoulli filter updating. Firstly, multiple peaks, including both mainlobe and sidelobe peaks, are selected to form the direction-of-arrival (DOA) measurement set, and then the Bernoulli filter is used to extract the target track. Secondly, to make full use of the informations in the beamforming output, not only the DOAs but also their intensities, the beam powers are used as the input measurement sets of the filter, and an approach based on Pearson correlation coefficient (PCC) is developed for distinguishing between signal and noise. Lastly, a hybrid method of the former two is proposed in the case of fewer then three arrays. The tracking performances of the three methods are compared in simulations and experiment. The simulations with three distributed arrays show that, compared with the DOA-based method, the beam-based method and the hybrid method can both improve the target tracking accuracy. The processing results of the shallow water experimental data collected by two arrays show that the hybrid method can achieve a better tracking performance.

scholarly journals Underwater Acoustic Target Recognition Based on Depthwise Separable Convolution Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1429
Author(s):  
Gang Hu ◽  
Kejun Wang ◽  
Liangliang Liu
Keyword(s):  
Feature Extraction ◽  
Target Recognition ◽  
Target Feature ◽  
Underwater Acoustic ◽  
Radiated Noise ◽  
Automatic Feature Extraction ◽  
Acoustic Target ◽  
And Cluster Analysis ◽  
Underwater Target ◽  
The One

Facing the complex marine environment, it is extremely challenging to conduct underwater acoustic target feature extraction and recognition using ship-radiated noise. In this paper, firstly, taking the one-dimensional time-domain raw signal of the ship as the input of the model, a new deep neural network model for underwater target recognition is proposed. Depthwise separable convolution and time-dilated convolution are used for passive underwater acoustic target recognition for the first time. The proposed model realizes automatic feature extraction from the raw data of ship radiated noise and temporal attention in the process of underwater target recognition. Secondly, the measured data are used to evaluate the model, and cluster analysis and visualization analysis are performed based on the features extracted from the model. The results show that the features extracted from the model have good characteristics of intra-class aggregation and inter-class separation. Furthermore, the cross-folding model is used to verify that there is no overfitting in the model, which improves the generalization ability of the model. Finally, the model is compared with traditional underwater acoustic target recognition, and its accuracy is significantly improved by 6.8%.

Combination of gated recurrent unit and Network in Network for underwater acoustic target recognition

2021 ◽  
Vol 263 (6) ◽  
pp. 486-492
Author(s):  
Shuang Yang ◽  
Xiangyang Zeng
Keyword(s):  
Information Acquisition ◽  
Target Recognition ◽  
Recognition Rate ◽  
Temporal Correlation ◽  
Feedback Mechanism ◽  
Underwater Acoustic ◽  
Acoustic Information ◽  
Proposed Model ◽  
Acoustic Target ◽  
Gated Recurrent Unit

Underwater acoustic target recognition is an important part of underwater acoustic signal processing and an important technical support for underwater acoustic information acquisition and underwater acoustic information confrontation. Taking into account that the gated recurrent unit (GRU) has an internal feedback mechanism that can reflect the temporal correlation of underwater acoustic target features, a model with gated recurrent unit and Network in Network (NIN) is proposed to recognize underwater acoustic targets in this paper. The proposed model introduces NIN to compress the hidden states of GRU while retaining the original timing characteristics of underwater acoustic target features. The higher recognition rate and faster calculation speed of the proposed model are demonstrated with experiments for raw underwater acoustic signals comparing with the multi-layer stacked GRU model.

Underwater Acoustic Target Recognition Based on Generative Adversarial Network Data Augmentation

2021 ◽  
Vol 263 (2) ◽  
pp. 4558-4564
Author(s):  
Minghong Zhang ◽  
Xinwei Luo
Keyword(s):  
Data Augmentation ◽  
Target Recognition ◽  
Training Data ◽  
Small Samples ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Underwater Acoustic ◽  
Adversarial Network ◽  
Acoustic Target ◽  
The Impact

Underwater acoustic target recognition is an important aspect of underwater acoustic research. In recent years, machine learning has been developed continuously, which is widely and effectively applied in underwater acoustic target recognition. In order to acquire good recognition results and reduce the problem of overfitting, Adequate data sets are essential. However, underwater acoustic samples are relatively rare, which has a certain impact on recognition accuracy. In this paper, in addition of the traditional audio data augmentation method, a new method of data augmentation using generative adversarial network is proposed, which uses generator and discriminator to learn the characteristics of underwater acoustic samples, so as to generate reliable underwater acoustic signals to expand the training data set. The expanded data set is input into the deep neural network, and the transfer learning method is applied to further reduce the impact caused by small samples by fixing part of the pre-trained parameters. The experimental results show that the recognition result of this method is better than the general underwater acoustic recognition method, and the effectiveness of this method is verified.

scholarly journals Vessel Tracking Using Bistatic Compact HFSWR

2020 ◽  
Vol 12 (8) ◽  
pp. 1266
Author(s):  
Weifeng Sun ◽  
Mengjie Ji ◽  
Weimin Huang ◽  
Yonggang Ji ◽  
Yongshou Dai
Keyword(s):  
Target Tracking ◽  
Development Trend ◽  
Target Range ◽  
Doppler Velocity ◽  
Detection Accuracy ◽  
Kinematic Parameters ◽  
Coverage Area ◽  
Detection And Tracking ◽  
Wave Radar ◽  
Target Track

Bistatic and multi-static high-frequency surface wave radar (HFSWR) is becoming a prospective development trend for sea surface surveillance due to its potential in extending the coverage area, improving the detection accuracy, etc. In this paper, the vessel detection and tracking performance of a newly developed bistatic compact HFSWR system whose transmitting and receiving antennas are not co-located was investigated. Firstly, the representation of the target range and Doppler velocity concerning a bistatic HFSWR was derived and compared with that of a monostatic system. Next, taking the characteristics of target kinematic parameters into account, a target tracking method applicable to a bistatic HFSWR is proposed. The simultaneous target tracking results from both monostatic and bistatic HFSWR field data are presented and compared. The experimental results demonstrate the good performance in target tracking of the bistatic HFSWR and also show that an HFSWR system combining monostatic and bistatic modes has the potential to enhance the target track continuity and improve the detection accuracy.

scholarly journals Convolutional Neural Network Using Kalman Filter for Human Detection and Tracking on RGB-D Video

2018 ◽  
Vol 12 (2) ◽  
pp. 105 ◽  
Author(s):  
Jovin Angelico ◽  
Ken Ratri Retno Wardani
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Convolutional Neural Network ◽  
Human Detection ◽  
Detection Accuracy ◽  
Tracking Accuracy ◽  
Lighting Condition ◽  
Detection And Tracking ◽  
Computation Efficiency ◽  
The Impact

The computer ability to detect human being by computer vision is still being improved both in accuracy or computation time. In low-lighting condition, the detection accuracy is usually low. This research uses additional information, besides RGB channels, namely a depth map that shows objects’ distance relative to the camera. This research integrates Cascade Classifier (CC) to localize the potential object, the Convolutional Neural Network (CNN) technique to identify the human and nonhuman image, and the Kalman filter technique to track human movement. For training and testing purposes, there are two kinds of RGB-D datasets used with different points of view and lighting conditions. Both datasets have been selected to remove images which contain a lot of noises and occlusions so that during the training process it will be more directed. Using these integrated techniques, detection and tracking accuracy reach 77.7%. The impact of using Kalman filter increases computation efficiency by 41%.

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