EM-based Algorithm for Unsupervised Clustering of Measurements from a Radar Sensor Network
| Title: | EM-based Algorithm for Unsupervised Clustering of Measurements from a Radar Sensor Network |
|---|---|
| Authors: | Yan, Linjie, Addabbo, Pia, Fiscante, Nicomino, Clemente, Carmine, Hao, Chengpeng, Giunta, Gaetano, Orlando, Danilo |
| Publication Year: | 2024 |
| Subject Terms: | Electrical Engineering and Systems Science - Signal Processing, 62, G.3 |
| More Details: | This paper deals with the problem of clustering data returned by a radar sensor network that monitors a region where multiple moving targets are present. The network is formed by nodes with limited functionalities that transmit the estimates of target positions (after a detection) to a fusion center without any association between measurements and targets. To solve the problem at hand, we resort to model-based learning algorithms and instead of applying the plain maximum likelihood approach, due to the related computational requirements, we exploit the latent variable model coupled with the expectation-maximization algorithm. The devised estimation procedure returns posterior probabilities that are used to cluster the huge amount of data collected by the fusion center. Remarkably, we also consider challenging scenarios with an unknown number of targets and estimate it by means of the model order selection rules. The clustering performance of the proposed strategy is compared to that of conventional data-driven methods over synthetic data. The numerical examples point out that the herein proposed solutions can provide reliable clustering performance overcoming the considered competitors. Comment: 12 pages 14 figures |
| Document Type: | Working Paper |
| Access URL: | http://arxiv.org/abs/2405.02643 |
| Accession Number: | edsarx.2405.02643 |
| Database: | arXiv |
| FullText | Text: Availability: 0 CustomLinks: – Url: http://arxiv.org/abs/2405.02643 Name: EDS - Arxiv Category: fullText Text: View this record from Arxiv MouseOverText: View this record from Arxiv – Url: https://resolver.ebsco.com/c/xy5jbn/result?sid=EBSCO:edsarx&genre=article&issn=&ISBN=&volume=&issue=&date=20240504&spage=&pages=&title=EM-based Algorithm for Unsupervised Clustering of Measurements from a Radar Sensor Network&atitle=EM-based%20Algorithm%20for%20Unsupervised%20Clustering%20of%20Measurements%20from%20a%20Radar%20Sensor%20Network&aulast=Yan%2C%20Linjie&id=DOI: Name: Full Text Finder (for New FTF UI) (s8985755) Category: fullText Text: Find It @ SCU Libraries MouseOverText: Find It @ SCU Libraries |
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| Items | – Name: Title Label: Title Group: Ti Data: EM-based Algorithm for Unsupervised Clustering of Measurements from a Radar Sensor Network – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yan%2C+Linjie%22">Yan, Linjie</searchLink><br /><searchLink fieldCode="AR" term="%22Addabbo%2C+Pia%22">Addabbo, Pia</searchLink><br /><searchLink fieldCode="AR" term="%22Fiscante%2C+Nicomino%22">Fiscante, Nicomino</searchLink><br /><searchLink fieldCode="AR" term="%22Clemente%2C+Carmine%22">Clemente, Carmine</searchLink><br /><searchLink fieldCode="AR" term="%22Hao%2C+Chengpeng%22">Hao, Chengpeng</searchLink><br /><searchLink fieldCode="AR" term="%22Giunta%2C+Gaetano%22">Giunta, Gaetano</searchLink><br /><searchLink fieldCode="AR" term="%22Orlando%2C+Danilo%22">Orlando, Danilo</searchLink> – Name: DatePubCY Label: Publication Year Group: Date Data: 2024 – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Electrical+Engineering+and+Systems+Science+-+Signal+Processing%22">Electrical Engineering and Systems Science - Signal Processing</searchLink><br /><searchLink fieldCode="DE" term="%2262%22">62</searchLink><br /><searchLink fieldCode="DE" term="%22G%2E3%22">G.3</searchLink> – Name: Abstract Label: Description Group: Ab Data: This paper deals with the problem of clustering data returned by a radar sensor network that monitors a region where multiple moving targets are present. The network is formed by nodes with limited functionalities that transmit the estimates of target positions (after a detection) to a fusion center without any association between measurements and targets. To solve the problem at hand, we resort to model-based learning algorithms and instead of applying the plain maximum likelihood approach, due to the related computational requirements, we exploit the latent variable model coupled with the expectation-maximization algorithm. The devised estimation procedure returns posterior probabilities that are used to cluster the huge amount of data collected by the fusion center. Remarkably, we also consider challenging scenarios with an unknown number of targets and estimate it by means of the model order selection rules. The clustering performance of the proposed strategy is compared to that of conventional data-driven methods over synthetic data. The numerical examples point out that the herein proposed solutions can provide reliable clustering performance overcoming the considered competitors.<br />Comment: 12 pages 14 figures – Name: TypeDocument Label: Document Type Group: TypDoc Data: Working Paper – Name: URL Label: Access URL Group: URL Data: <link linkTarget="URL" linkTerm="http://arxiv.org/abs/2405.02643" linkWindow="_blank">http://arxiv.org/abs/2405.02643</link> – Name: AN Label: Accession Number Group: ID Data: edsarx.2405.02643 |
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| RecordInfo | BibRecord: BibEntity: Subjects: – SubjectFull: Electrical Engineering and Systems Science - Signal Processing Type: general – SubjectFull: 62 Type: general – SubjectFull: G.3 Type: general Titles: – TitleFull: EM-based Algorithm for Unsupervised Clustering of Measurements from a Radar Sensor Network Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yan, Linjie – PersonEntity: Name: NameFull: Addabbo, Pia – PersonEntity: Name: NameFull: Fiscante, Nicomino – PersonEntity: Name: NameFull: Clemente, Carmine – PersonEntity: Name: NameFull: Hao, Chengpeng – PersonEntity: Name: NameFull: Giunta, Gaetano – PersonEntity: Name: NameFull: Orlando, Danilo IsPartOfRelationships: – BibEntity: Dates: – D: 04 M: 05 Type: published Y: 2024 |
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