A Weighted Decision-Level Fusion Architecture for Ballistic Target Classification in Midcourse Phase
The recognition of warheads in the target cloud of the ballistic midcourse phase remains a challenging issue for missile defense systems. Considering factors such as the differing dimensions of the features between sensors and the different recognition credibility of each sensor, this paper proposes...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/17/6649 |
| _version_ | 1797493014984130560 |
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| author | Nannan Wei Limin Zhang Xinggan Zhang |
| author_facet | Nannan Wei Limin Zhang Xinggan Zhang |
| author_sort | Nannan Wei |
| collection | DOAJ |
| description | The recognition of warheads in the target cloud of the ballistic midcourse phase remains a challenging issue for missile defense systems. Considering factors such as the differing dimensions of the features between sensors and the different recognition credibility of each sensor, this paper proposes a weighted decision-level fusion architecture to take advantage of data from multiple radar sensors, and an online feature reliability evaluation method is also used to comprehensively generate sensor weight coefficients. The weighted decision-level fusion method can overcome the deficiency of a single sensor and enhance the recognition rate for warheads in the midcourse phase by considering the changes in the reliability of the sensor’s performance caused by the influence of the environment, location, and other factors during observation. Based on the simulation dataset, the experiment was carried out with multiple sensors and multiple bandwidths, and the results showed that the proposed model could work well with various classifiers involving traditional learning algorithms and ensemble learning algorithms. |
| first_indexed | 2024-03-10T01:13:55Z |
| format | Article |
| id | doaj.art-29ce488b49584a6592acb65819c09b7e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T01:13:55Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-29ce488b49584a6592acb65819c09b7e2023-11-23T14:12:05ZengMDPI AGSensors1424-82202022-09-012217664910.3390/s22176649A Weighted Decision-Level Fusion Architecture for Ballistic Target Classification in Midcourse PhaseNannan Wei0Limin Zhang1Xinggan Zhang2School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, ChinaSchool of Electronic Science and Engineering, Nanjing University, Nanjing 210023, ChinaSchool of Electronic Science and Engineering, Nanjing University, Nanjing 210023, ChinaThe recognition of warheads in the target cloud of the ballistic midcourse phase remains a challenging issue for missile defense systems. Considering factors such as the differing dimensions of the features between sensors and the different recognition credibility of each sensor, this paper proposes a weighted decision-level fusion architecture to take advantage of data from multiple radar sensors, and an online feature reliability evaluation method is also used to comprehensively generate sensor weight coefficients. The weighted decision-level fusion method can overcome the deficiency of a single sensor and enhance the recognition rate for warheads in the midcourse phase by considering the changes in the reliability of the sensor’s performance caused by the influence of the environment, location, and other factors during observation. Based on the simulation dataset, the experiment was carried out with multiple sensors and multiple bandwidths, and the results showed that the proposed model could work well with various classifiers involving traditional learning algorithms and ensemble learning algorithms.https://www.mdpi.com/1424-8220/22/17/6649ballistic missile defensetarget classificationmulti-sensor data fusiononline feature evaluationweighted decision-level fusion |
| spellingShingle | Nannan Wei Limin Zhang Xinggan Zhang A Weighted Decision-Level Fusion Architecture for Ballistic Target Classification in Midcourse Phase Sensors ballistic missile defense target classification multi-sensor data fusion online feature evaluation weighted decision-level fusion |
| title | A Weighted Decision-Level Fusion Architecture for Ballistic Target Classification in Midcourse Phase |
| title_full | A Weighted Decision-Level Fusion Architecture for Ballistic Target Classification in Midcourse Phase |
| title_fullStr | A Weighted Decision-Level Fusion Architecture for Ballistic Target Classification in Midcourse Phase |
| title_full_unstemmed | A Weighted Decision-Level Fusion Architecture for Ballistic Target Classification in Midcourse Phase |
| title_short | A Weighted Decision-Level Fusion Architecture for Ballistic Target Classification in Midcourse Phase |
| title_sort | weighted decision level fusion architecture for ballistic target classification in midcourse phase |
| topic | ballistic missile defense target classification multi-sensor data fusion online feature evaluation weighted decision-level fusion |
| url | https://www.mdpi.com/1424-8220/22/17/6649 |
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