An Analysis of Clustering Tools for Moving Target Indication

Report No. ARL-TR-5037
Authors: Anthony Martone, Roberto Innocenti, and Kenneth Ranney
Date/Pages: November 2009; 34 pages
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, ultrawideband radar. Our MTI processing algorithms include change detection (CD), used to identify the MT signature; automatic target detection (ATD), used to eliminate imaging artifacts and potential false alarms due to target multi-bounce effects; clustering, used to identify a centroid for each cluster in the ATD output images; and tracking, used to establish a trajectory of the MT. These 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 report, we investigate two techniques that automatically determine the number of clusters: the knee-point (KP) algorithm and the recursive pixel finding (RPF) algorithm. The KP algorithm is a well-known heuristic approach for determining the number of clusters. The RPF algorithm is analogous to the image processing, pixel labeling procedure. Both routines processed data collected by our low-frequency, ultrawideband radar and their results are compared.
Distribution: Approved for public release
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Last Update / Reviewed: November 1, 2009