Clustering analysis of moving target signatures

Anthony Martone; Kenneth Ranney; Roberto Innocenti

doi:10.1117/12.852549

27 April 2010 Clustering analysis of moving target signatures

Anthony Martone, Kenneth Ranney, Roberto Innocenti

Proceedings Volume 7669, Radar Sensor Technology XIV; 766912 (2010) https://doi.org/10.1117/12.852549
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

Previously, we developed a moving target indication (MTI) processing approach to detect and track slow-moving targets inside buildings, which successfully detected moving targets (MTs) from data collected by a low-frequency, ultra-wideband radar. Our MTI algorithms include change detection, automatic target detection (ATD), clustering, and tracking. The MTI algorithms can be implemented in a real-time or near-real-time system; however, a person-in-the-loop is needed to select input parameters for the clustering algorithm. Specifically, the number of clusters to input into the cluster algorithm is unknown and requires manual selection. A critical need exists to automate all aspects of the MTI processing formulation. In this paper, we investigate two techniques that automatically determine the number of clusters: the adaptive knee-point (KP) algorithm and the recursive pixel finding (RPF) algorithm. The KP algorithm is based on a well-known heuristic approach for determining the number of clusters. The RPF algorithm is analogous to the image processing, pixel labeling procedure. Both algorithms are used to analyze the false alarm and detection rates of three operational scenarios of personnel walking inside wood and cinderblock buildings.

Citation Download Citation

Anthony Martone, Kenneth Ranney, and Roberto Innocenti "Clustering analysis of moving target signatures", Proc. SPIE 7669, Radar Sensor Technology XIV, 766912 (27 April 2010); https://doi.org/10.1117/12.852549

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