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

TL;DR

This paper introduces an EM-based unsupervised clustering algorithm for radar sensor network data, effectively handling unknown target counts and outperforming traditional methods in synthetic scenarios.

Contribution

It presents a novel EM algorithm for clustering radar measurements with unknown target numbers, improving computational efficiency and clustering accuracy.

Findings

Reliable clustering performance demonstrated over synthetic data

Outperforms conventional data-driven methods

Effectively estimates the number of targets

Abstract

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.