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

• Published in: IEEE transactions on aerospace and electronic systems Vol. 61; no. 1; pp. 787 - 801
• Main Authors: Yan, Linjie, Addabbo, Pia, Fiscante, Nicomino, Clemente, Carmine, Hao, Chengpeng, Giunta, Gaetano, Orlando, Danilo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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
• DOI: 10.1109/TAES.2024.3448390

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.