Full metadata record
DC Field | Value | Language |
---|---|---|
dc.creator | Shi, Y. (Yifang) | - |
dc.creator | Xue, M. (Mengfan) | - |
dc.creator | Ding, Y. (Yuemin) | - |
dc.creator | Peng, D. (Dongliang) | - |
dc.date.accessioned | 2023-02-04T10:37:46Z | - |
dc.date.available | 2023-02-04T10:37:46Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Shi, Y. (Yifang); Xue, M. (Mengfan); Ding, Y. (Yuemin); et al. "Improved multitarget tracking in clutter using bearings-only measurements". Sensors. 18 (6), 2018, 1772 | es |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | https://hdl.handle.net/10171/65268 | - |
dc.description.abstract | Multitarget tracking in clutter using bearings-only measurements is a challenging problem. In this paper, a performance improved nonlinear filter is proposed on the basis of the Random Finite Set (RFS) theory and is named as Gaussian mixture measurements-based cardinality probability hypothesis density (GMMbCPHD) filter. The GMMbCPHD filter enables to address two main issues: measurement-origin-uncertainty and measurement nonlinearity, which constitutes the key problems in bearings-only multitarget tracking in clutter. For the measurement-origin-uncertainty issue, the proposed filter estimates the intensity of RFS of multiple targets as well as propagates the posterior cardinality distribution. For the measurement-origin-nonlinearity issue, the GMMbCPHD approximates the measurement likelihood function using a Gaussian mixture rather than a single Gaussian distribution as used in extended Kalman filter (EKF). The superiority of the proposed GMMbCPHD are validated by comparing with several state-of-the-art algorithms via intensive simulation studies. | es_ES |
dc.description.sponsorship | This work was surported by the National Natural Science Foundation of China (grant No. 61702369 and grant No. 61703131). | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.subject | Bearings-only | es_ES |
dc.subject | Multitarget tracking | es_ES |
dc.subject | Measurement-origin-uncertainty | es_ES |
dc.subject | Measurement nonlinearity | es_ES |
dc.subject | Gaussian mixture measurements-cardinality probability hypothesis density | es_ES |
dc.title | Improved multitarget tracking in clutter using bearings-only measurements | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.description.note | This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | es_ES |
dc.identifier.doi | 10.3390/s18061772 | - |
dadun.citation.number | 6 | es_ES |
dadun.citation.publicationName | Sensors | es_ES |
dadun.citation.startingPage | 1772 | es_ES |
dadun.citation.volume | 18 | es_ES |
dc.identifier.pmid | 29865152 | - |
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