Tracking of ARPA Radar Signals Based on UK–PDAF and Fusion with AIS Data
To maintain the existing systems of ships and introduce autonomous operation technology, it is necessary to improve situational awareness through the sensor fusion of the automatic identification system (AIS) and automatic radar plotting aid (ARPA), which are installed sensors. This study proposes a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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The Korean Society of Ocean Engineers
2023-02-01
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| Series: | 한국해양공학회지 |
| Subjects: | |
| Online Access: | https://www.joet.org/journal/view.php?number=3100 |
| _version_ | 1811163397065015296 |
|---|---|
| author | Chan Woo Han Sung Wook Lee Eun Seok Jin |
| author_facet | Chan Woo Han Sung Wook Lee Eun Seok Jin |
| author_sort | Chan Woo Han |
| collection | DOAJ |
| description | To maintain the existing systems of ships and introduce autonomous operation technology, it is necessary to improve situational awareness through the sensor fusion of the automatic identification system (AIS) and automatic radar plotting aid (ARPA), which are installed sensors. This study proposes an algorithm for determining whether AIS and ARPA signals are sent to the same ship in real time. To minimize the number of errors caused by the time series and abnormal phenomena of heterogeneous signals, a tracking method based on the combination of the unscented Kalman filter and probabilistic data association filter is performed on ARPA radar signals, and a position prediction method is applied to AIS signals. Especially, the proposed algorithm determines whether the signal is for the same vessel by comparing motion-related components among data of heterogeneous signals to which the corresponding method is applied. Finally, a measurement test is conducted on a training ship. In this process, the proposed algorithm is validated using the AIS and ARPA signal data received by the voyage data recorder for the same ship. In addition, the proposed algorithm is verified by comparing the test results with those obtained from raw data. Therefore, it is recommended to use a sensor fusion algorithm that considers the characteristics of sensors to improve the situational awareness accuracy of existing ship systems. |
| first_indexed | 2024-04-10T06:44:35Z |
| format | Article |
| id | doaj.art-ffb567c273834083a17f5cb39aeb5660 |
| institution | Directory Open Access Journal |
| issn | 1225-0767 2287-6715 |
| language | English |
| last_indexed | 2024-04-10T06:44:35Z |
| publishDate | 2023-02-01 |
| publisher | The Korean Society of Ocean Engineers |
| record_format | Article |
| series | 한국해양공학회지 |
| spelling | doaj.art-ffb567c273834083a17f5cb39aeb56602023-02-28T13:44:20ZengThe Korean Society of Ocean Engineers한국해양공학회지1225-07672287-67152023-02-01371384810.26748/KSOE.2022.046Tracking of ARPA Radar Signals Based on UK–PDAF and Fusion with AIS DataChan Woo Han0https://orcid.org/0000-0001-8559-022XSung Wook Lee1https://orcid.org/0000-0001-6089-303XEun Seok Jin2https://orcid.org/0000-0002-0388-3748Korea Maritime and Ocean UniversityKorea Maritime and Ocean UniversityDaewoo Shipbuilding & Marine Engineering Co. Ltd.To maintain the existing systems of ships and introduce autonomous operation technology, it is necessary to improve situational awareness through the sensor fusion of the automatic identification system (AIS) and automatic radar plotting aid (ARPA), which are installed sensors. This study proposes an algorithm for determining whether AIS and ARPA signals are sent to the same ship in real time. To minimize the number of errors caused by the time series and abnormal phenomena of heterogeneous signals, a tracking method based on the combination of the unscented Kalman filter and probabilistic data association filter is performed on ARPA radar signals, and a position prediction method is applied to AIS signals. Especially, the proposed algorithm determines whether the signal is for the same vessel by comparing motion-related components among data of heterogeneous signals to which the corresponding method is applied. Finally, a measurement test is conducted on a training ship. In this process, the proposed algorithm is validated using the AIS and ARPA signal data received by the voyage data recorder for the same ship. In addition, the proposed algorithm is verified by comparing the test results with those obtained from raw data. Therefore, it is recommended to use a sensor fusion algorithm that considers the characteristics of sensors to improve the situational awareness accuracy of existing ship systems.https://www.joet.org/journal/view.php?number=3100sensor fusionaisradarunscented kalman filterprobabilistic data association filter |
| spellingShingle | Chan Woo Han Sung Wook Lee Eun Seok Jin Tracking of ARPA Radar Signals Based on UK–PDAF and Fusion with AIS Data 한국해양공학회지 sensor fusion ais radar unscented kalman filter probabilistic data association filter |
| title | Tracking of ARPA Radar Signals Based on UK–PDAF and Fusion with AIS Data |
| title_full | Tracking of ARPA Radar Signals Based on UK–PDAF and Fusion with AIS Data |
| title_fullStr | Tracking of ARPA Radar Signals Based on UK–PDAF and Fusion with AIS Data |
| title_full_unstemmed | Tracking of ARPA Radar Signals Based on UK–PDAF and Fusion with AIS Data |
| title_short | Tracking of ARPA Radar Signals Based on UK–PDAF and Fusion with AIS Data |
| title_sort | tracking of arpa radar signals based on uk pdaf and fusion with ais data |
| topic | sensor fusion ais radar unscented kalman filter probabilistic data association filter |
| url | https://www.joet.org/journal/view.php?number=3100 |
| work_keys_str_mv | AT chanwoohan trackingofarparadarsignalsbasedonukpdafandfusionwithaisdata AT sungwooklee trackingofarparadarsignalsbasedonukpdafandfusionwithaisdata AT eunseokjin trackingofarparadarsignalsbasedonukpdafandfusionwithaisdata |