A novel density-based clustering method for effective removal of spurious intersections in bearings-only localization
Abstract In bearings-only localization, clustering-based methods have been widely used to remove spurious intersections by fusing multiple bearing measurements from different observation stations. Existing clustering methods, including fuzzy C-mean (FCM) clustering and density-based spatial clusteri...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-02-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13634-023-00974-8 |
| _version_ | 1811171513328467968 |
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| author | Zhian Deng Zhiguo Wang Tianbao Zhang Chunjie Zhang Weijian Si |
| author_facet | Zhian Deng Zhiguo Wang Tianbao Zhang Chunjie Zhang Weijian Si |
| author_sort | Zhian Deng |
| collection | DOAJ |
| description | Abstract In bearings-only localization, clustering-based methods have been widely used to remove spurious intersections by fusing multiple bearing measurements from different observation stations. Existing clustering methods, including fuzzy C-mean (FCM) clustering and density-based spatial clustering of applications with noise (DBSCAN), must specify the number of clusters and the threshold for defining the neighborhood density, respectively, which are always unknown and difficult to estimate. Moreover, in dense radiation source scenes, existing clustering methods for removal of spurious intersections all deteriorate significantly. Therefore, we propose a novel density-based clustering method called K-M-DBSCAN, which combines the minimum K distance algorithm with Mahalanobis distance-based DBSCAN clustering. Firstly, K-M-DBSCAN uses minimum K distance algorithm for preprocessing to remove most of the spurious intersections and reduce the computational complexity of clustering. Mahalanobis distance-based DBSCAN is used for clustering and spurious intersections recognition. In order to adapt the large variations of sample density in clustering, we use Mahalanobis distance to define an explicit neighborhood of DBSCAN instead of traditional Euclidean distance. Simulation results show that the proposed K-M-DBSCAN performs better than FCM and DBSCAN in removing of spurious intersections. |
| first_indexed | 2024-04-10T17:15:15Z |
| format | Article |
| id | doaj.art-8279ad4baa08435c8a2dcfe767da9961 |
| institution | Directory Open Access Journal |
| issn | 1687-6180 |
| language | English |
| last_indexed | 2024-04-10T17:15:15Z |
| publishDate | 2023-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-8279ad4baa08435c8a2dcfe767da99612023-02-05T12:26:57ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61802023-02-012023112110.1186/s13634-023-00974-8A novel density-based clustering method for effective removal of spurious intersections in bearings-only localizationZhian Deng0Zhiguo Wang1Tianbao Zhang2Chunjie Zhang3Weijian Si4College of Information and communication Engineering, Harbin Engineering UniversityCollege of Information and communication Engineering, Harbin Engineering UniversityCollege of Information and communication Engineering, Harbin Engineering UniversityCollege of Information and communication Engineering, Harbin Engineering UniversityCollege of Information and communication Engineering, Harbin Engineering UniversityAbstract In bearings-only localization, clustering-based methods have been widely used to remove spurious intersections by fusing multiple bearing measurements from different observation stations. Existing clustering methods, including fuzzy C-mean (FCM) clustering and density-based spatial clustering of applications with noise (DBSCAN), must specify the number of clusters and the threshold for defining the neighborhood density, respectively, which are always unknown and difficult to estimate. Moreover, in dense radiation source scenes, existing clustering methods for removal of spurious intersections all deteriorate significantly. Therefore, we propose a novel density-based clustering method called K-M-DBSCAN, which combines the minimum K distance algorithm with Mahalanobis distance-based DBSCAN clustering. Firstly, K-M-DBSCAN uses minimum K distance algorithm for preprocessing to remove most of the spurious intersections and reduce the computational complexity of clustering. Mahalanobis distance-based DBSCAN is used for clustering and spurious intersections recognition. In order to adapt the large variations of sample density in clustering, we use Mahalanobis distance to define an explicit neighborhood of DBSCAN instead of traditional Euclidean distance. Simulation results show that the proposed K-M-DBSCAN performs better than FCM and DBSCAN in removing of spurious intersections.https://doi.org/10.1186/s13634-023-00974-8Passive positioningMahalanobis distanceDensity clustering |
| spellingShingle | Zhian Deng Zhiguo Wang Tianbao Zhang Chunjie Zhang Weijian Si A novel density-based clustering method for effective removal of spurious intersections in bearings-only localization EURASIP Journal on Advances in Signal Processing Passive positioning Mahalanobis distance Density clustering |
| title | A novel density-based clustering method for effective removal of spurious intersections in bearings-only localization |
| title_full | A novel density-based clustering method for effective removal of spurious intersections in bearings-only localization |
| title_fullStr | A novel density-based clustering method for effective removal of spurious intersections in bearings-only localization |
| title_full_unstemmed | A novel density-based clustering method for effective removal of spurious intersections in bearings-only localization |
| title_short | A novel density-based clustering method for effective removal of spurious intersections in bearings-only localization |
| title_sort | novel density based clustering method for effective removal of spurious intersections in bearings only localization |
| topic | Passive positioning Mahalanobis distance Density clustering |
| url | https://doi.org/10.1186/s13634-023-00974-8 |
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