EM-Based Algorithm for Unsupervised Clustering of Measurements from a Radar Sensor Network

This article 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 cent...

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Veröffentlicht in:IEEE transactions on aerospace and electronic systems Jg. 61; H. 1; S. 787 - 801
Hauptverfasser: Yan, Linjie, Addabbo, Pia, Fiscante, Nicomino, Clemente, Carmine, Hao, Chengpeng, Giunta, Gaetano, Orlando, Danilo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.02.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Batch algorithms
Clustering
Clustering algorithms
Computational modeling
expectation–maximization (EM)
Machine learning
measurement clustering
Moving targets
multiple moving targets
Radar
Radar detection
Radar tracking
Random variables
sensor network
Synthetic data
Target detection
Target tracking
Time measurement
unsupervised learning
ISSN:0018-9251, 1557-9603
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Zusammenfassung:This article 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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2024.3448390