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Proceedings Paper

Clustering analysis of moving target signatures
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Paper 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.

Paper Details

Date Published: 27 April 2010
PDF: 12 pages
Proc. SPIE 7669, Radar Sensor Technology XIV, 766912 (27 April 2010); doi: 10.1117/12.852549
Show Author Affiliations
Anthony Martone, U.S. Army Research Lab. (United States)
Kenneth Ranney, U.S. Army Research Lab. (United States)
Roberto Innocenti, U.S. Army Research Lab. (United States)


Published in SPIE Proceedings Vol. 7669:
Radar Sensor Technology XIV
Kenneth I. Ranney; Armin W. Doerry, Editor(s)

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