Target depth estimation by deep neural network based on acoustic interference structure in deep water
Abstract Automatic and robust target depth estimation is an important issue of an active detection system working in the reliable acoustic path (RAP) environment. In this paper, the target depth‐sensitive acoustic interference structure is used as input, and a deep neural network (DNN) method is pro...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-07-01
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| Series: | IET Radar, Sonar & Navigation |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/rsn2.12248 |
| _version_ | 1811249556498677760 |
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| author | Yue Guo Rui Duan Kunde Yang |
| author_facet | Yue Guo Rui Duan Kunde Yang |
| author_sort | Yue Guo |
| collection | DOAJ |
| description | Abstract Automatic and robust target depth estimation is an important issue of an active detection system working in the reliable acoustic path (RAP) environment. In this paper, the target depth‐sensitive acoustic interference structure is used as input, and a deep neural network (DNN) method is proposed to realize automatic depth estimation. Furthermore, to improve the robustness of the network, both the sound speed profile (SSP) uncertainty and the target trajectory uncertainty are considered in the training dataset. The former is modelled as a random weighted sum of the empirical orthogonal function (EOF), and the latter is modelled by the partial‐data‐lacking of the network input. The method was evaluated through simulated data and compared with the conventional method of matching the measured interference structure with the replicas. The presented method exhibits a higher accuracy of depth estimate in uncertain environments when the signal‐to‐noise ratio (SNR) is higher than −6 dB, but its performance deteriorates rapidly as the SNR decreases further. The method is also verified by semi‐simulation with experimental data in deep water. |
| first_indexed | 2024-04-12T15:49:52Z |
| format | Article |
| id | doaj.art-8d5574279eb24dce830e5350632887fe |
| institution | Directory Open Access Journal |
| issn | 1751-8784 1751-8792 |
| language | English |
| last_indexed | 2024-04-12T15:49:52Z |
| publishDate | 2022-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Radar, Sonar & Navigation |
| spelling | doaj.art-8d5574279eb24dce830e5350632887fe2022-12-22T03:26:33ZengWileyIET Radar, Sonar & Navigation1751-87841751-87922022-07-011671131114310.1049/rsn2.12248Target depth estimation by deep neural network based on acoustic interference structure in deep waterYue Guo0Rui Duan1Kunde Yang2School of Marine Science and Technology Northwestern Polytechnical University Xi'an Shaanxi ChinaSchool of Marine Science and Technology Northwestern Polytechnical University Xi'an Shaanxi ChinaSchool of Marine Science and Technology Northwestern Polytechnical University Xi'an Shaanxi ChinaAbstract Automatic and robust target depth estimation is an important issue of an active detection system working in the reliable acoustic path (RAP) environment. In this paper, the target depth‐sensitive acoustic interference structure is used as input, and a deep neural network (DNN) method is proposed to realize automatic depth estimation. Furthermore, to improve the robustness of the network, both the sound speed profile (SSP) uncertainty and the target trajectory uncertainty are considered in the training dataset. The former is modelled as a random weighted sum of the empirical orthogonal function (EOF), and the latter is modelled by the partial‐data‐lacking of the network input. The method was evaluated through simulated data and compared with the conventional method of matching the measured interference structure with the replicas. The presented method exhibits a higher accuracy of depth estimate in uncertain environments when the signal‐to‐noise ratio (SNR) is higher than −6 dB, but its performance deteriorates rapidly as the SNR decreases further. The method is also verified by semi‐simulation with experimental data in deep water.https://doi.org/10.1049/rsn2.12248active sonardeep neural networkinterference structurereliable acoustic pathtarget depth estimation |
| spellingShingle | Yue Guo Rui Duan Kunde Yang Target depth estimation by deep neural network based on acoustic interference structure in deep water IET Radar, Sonar & Navigation active sonar deep neural network interference structure reliable acoustic path target depth estimation |
| title | Target depth estimation by deep neural network based on acoustic interference structure in deep water |
| title_full | Target depth estimation by deep neural network based on acoustic interference structure in deep water |
| title_fullStr | Target depth estimation by deep neural network based on acoustic interference structure in deep water |
| title_full_unstemmed | Target depth estimation by deep neural network based on acoustic interference structure in deep water |
| title_short | Target depth estimation by deep neural network based on acoustic interference structure in deep water |
| title_sort | target depth estimation by deep neural network based on acoustic interference structure in deep water |
| topic | active sonar deep neural network interference structure reliable acoustic path target depth estimation |
| url | https://doi.org/10.1049/rsn2.12248 |
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