Research on Underwater Source Ranging Algorithm Based on Joint Distribution Adaptation

LI Li; SUN Yulin; CAO Ran; GUO Longxiang

doi:10.11999/JEIT211418

Volume 44 Issue 6

Jun. 2022

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LI Li, SUN Yulin, CAO Ran, GUO Longxiang. Research on Underwater Source Ranging Algorithm Based on Joint Distribution Adaptation[J]. Journal of Electronics & Information Technology, 2022, 44(6): 2061-2070. doi: 10.11999/JEIT211418

Citation:

LI Li, SUN Yulin, CAO Ran, GUO Longxiang. Research on Underwater Source Ranging Algorithm Based on Joint Distribution Adaptation[J]. Journal of Electronics & Information Technology, 2022, 44(6): 2061-2070. doi: 10.11999/JEIT211418

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Research on Underwater Source Ranging Algorithm Based on Joint Distribution Adaptation

doi: 10.11999/JEIT211418

LI Li^{1, 2, 3},
SUN Yulin^{1, 2, 3},
CAO Ran^{1, 2, 3, 4
,
,},
GUO Longxiang^{1, 2, 3}

1.
Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, China
2.
Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University), Ministry of Industry and Information Technology, Harbin 150001, China
3.
College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China
4.
College of Underwater Acoustic Engineering, Qingdao Innovation and Development Center of Harbin Engineering University, Qingdao 266400, China

Funds: The National Natural Science Foundation of China (52071111, 51779061)

Received Date: 2021-12-02
Accepted Date: 2022-05-05
Rev Recd Date: 2022-05-03

Available Online: 2022-05-11

Publish Date: 2022-06-21

Abstract

Abstract

Underwater source passive ranging is based on the pressure radiated by the source in the received data. It is a parameter estimation problem to search for source position parameters in the airspace through the method. Parameter estimation problems are usually converted into classification problems by machine learning methods, which have more accurate estimation capabilities than traditional Matched Field Processing (MFP) and with needless prior sound field information. However, when the probability density of training data and test data follow different distributions or the training data is insufficient, the effect of the classifier under traditional machine learning methods is usually poor. Therefore, an underwater target source ranging algorithm based on Joint Distribution Adaptation (JDA) is proposed to find an appropriate transformation matrix for data mapping, thereby reducing the distribution differences and realizing the migration between source and target. The experimental results indicate that JDA can effectively reduce the differences between the track data obtained in the underwater acoustic field at different times and orientations, thus target could be predicted by classifier based on the source training. The resulting Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are reduced by more than 30%, enabling more accurate distance estimates.
- Underwater source ranging,
- Joint Distribution Adaptation (JDA),
- K-Nearest Neighbor (KNN),
- Support Vector Machine (SVM),
- Convolutional Neural Network (CNN)

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References(26)

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