Accurate Identification for CW Direct Signal in Underwater Acoustic Ranging
The underwater channel is bilateral, heterogeneous, uncertain, and exhibits multipath transmission, sound line curvature, etc. These properties complicate the structure of the received pulse, causing great challenges in direct signal identification for ranging purposes and impacts on back-end data p...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/3/454 |
| _version_ | 1827305911125803008 |
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| author | Jing Li Jin Fu Nan Zou |
| author_facet | Jing Li Jin Fu Nan Zou |
| author_sort | Jing Li |
| collection | DOAJ |
| description | The underwater channel is bilateral, heterogeneous, uncertain, and exhibits multipath transmission, sound line curvature, etc. These properties complicate the structure of the received pulse, causing great challenges in direct signal identification for ranging purposes and impacts on back-end data processing, even accurate acoustic positioning. Machine learning (ML) combined with underwater acoustics has emerged as a prominent area of research in recent years. From a statistical perspective, ML can be viewed as an optimization strategy. Nevertheless, the existing ML-based direct-signal discrimination approaches rely on independent assessment, utilizing a single sensor (beacon or buoy), which is still insufficient for adapting to the complex underwater environment. Thus, discrimination accuracy decreases. To address the above issues, an accurate CW direct signal detection approach is performed using the decision tree algorithm, which belongs to ML. Initially, the pulse parameter characteristics in the underwater multipath channel are investigated and the parameter models are built. Then, based on multi-sensor localization performance feedback, fusion characteristics for diverse pulse are created. Next, the pulse parameter characteristics are preprocessed to mitigate the impact of varying magnitudes and units of magnitude on data processing. Then, the decision tree is built to obtain the desired output results and realize accurate recognition of the ranging direct signals. Finally, the feasibility and reliability of this paper’s method are verified by computer simulation and field testing. |
| first_indexed | 2024-04-24T18:07:02Z |
| format | Article |
| id | doaj.art-6ce06e4ca371428f902b9c09c47f4175 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-04-24T18:07:02Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-6ce06e4ca371428f902b9c09c47f41752024-03-27T13:49:18ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-03-0112345410.3390/jmse12030454Accurate Identification for CW Direct Signal in Underwater Acoustic RangingJing Li0Jin Fu1Nan Zou2National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, ChinaNational Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, ChinaNational Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, ChinaThe underwater channel is bilateral, heterogeneous, uncertain, and exhibits multipath transmission, sound line curvature, etc. These properties complicate the structure of the received pulse, causing great challenges in direct signal identification for ranging purposes and impacts on back-end data processing, even accurate acoustic positioning. Machine learning (ML) combined with underwater acoustics has emerged as a prominent area of research in recent years. From a statistical perspective, ML can be viewed as an optimization strategy. Nevertheless, the existing ML-based direct-signal discrimination approaches rely on independent assessment, utilizing a single sensor (beacon or buoy), which is still insufficient for adapting to the complex underwater environment. Thus, discrimination accuracy decreases. To address the above issues, an accurate CW direct signal detection approach is performed using the decision tree algorithm, which belongs to ML. Initially, the pulse parameter characteristics in the underwater multipath channel are investigated and the parameter models are built. Then, based on multi-sensor localization performance feedback, fusion characteristics for diverse pulse are created. Next, the pulse parameter characteristics are preprocessed to mitigate the impact of varying magnitudes and units of magnitude on data processing. Then, the decision tree is built to obtain the desired output results and realize accurate recognition of the ranging direct signals. Finally, the feasibility and reliability of this paper’s method are verified by computer simulation and field testing.https://www.mdpi.com/2077-1312/12/3/454machine learningdirect signal identificationinformation fusionperformance feedback |
| spellingShingle | Jing Li Jin Fu Nan Zou Accurate Identification for CW Direct Signal in Underwater Acoustic Ranging Journal of Marine Science and Engineering machine learning direct signal identification information fusion performance feedback |
| title | Accurate Identification for CW Direct Signal in Underwater Acoustic Ranging |
| title_full | Accurate Identification for CW Direct Signal in Underwater Acoustic Ranging |
| title_fullStr | Accurate Identification for CW Direct Signal in Underwater Acoustic Ranging |
| title_full_unstemmed | Accurate Identification for CW Direct Signal in Underwater Acoustic Ranging |
| title_short | Accurate Identification for CW Direct Signal in Underwater Acoustic Ranging |
| title_sort | accurate identification for cw direct signal in underwater acoustic ranging |
| topic | machine learning direct signal identification information fusion performance feedback |
| url | https://www.mdpi.com/2077-1312/12/3/454 |
| work_keys_str_mv | AT jingli accurateidentificationforcwdirectsignalinunderwateracousticranging AT jinfu accurateidentificationforcwdirectsignalinunderwateracousticranging AT nanzou accurateidentificationforcwdirectsignalinunderwateracousticranging |