Applications for the EM-Based Classifier in Radar Sensor Network

Abstract

In this letter, we focus on the application and analysis of the new model-based clustering architectures developed in our recent paper, where the analysis is limited to synthetic simulation results, to data collected by a real radar sensor. Specifically, a more comprehensive analysis of the proposed schemes is carried out in challenging real operating scenarios where the real measurements of multiple moving targets are not perfectly matched with the design assumptions due to real-world effects. Moreover, a new initialization procedure is introduced that accounts for multiple target velocities and the radar sampling time interval required by the specific application. Such a procedure is capable of providing the expectation-maximization (EM) procedure with reliable initial parameter values. The performance assessment confirms the effectiveness of these EM-based clustering algorithms not only on synthetic data, as observed in our companion paper, but also over real-recorded data and in comparison with suitable competitors.