An Analysis of Clustering Tools for Moving Target Indication
ARMY RESEARCH LAB ADELPHI MD SENSORS AND ELECTRON DEVICES DIRECTORATE
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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.
- Target Direction, Range and Position Finding