Object aggregation using merge-at-a-point algorithm

Kanupriya Salaria; Wiriyanto Darsono; Michael Hinman; Mark Linderman; Li Bai

doi:10.1117/12.538981

12 April 2004 Object aggregation using merge-at-a-point algorithm

Kanupriya Salaria, Wiriyanto Darsono, Michael Hinman, Mark Linderman, Li Bai

Proceedings Volume 5434, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2004; (2004) https://doi.org/10.1117/12.538981
Event: Defense and Security, 2004, Orlando, Florida, United States

Abstract

This paper describes a novel technique to detect military convoy’s moving patterns using the Ground Moving Target Indicator (GMTI) data. The specific pattern studied here is the moving vehicle groups that are merging onto a prescribed location. The algorithm can be used to detect the military convoy’s identity so that the situation can be assessed to prevent hostile enemy military advancements. The technique uses the minimum error solution (MES) to predict the point of intersection of vehicle tracks. Comparing this point of intersection to the prescribed location it can be determined whether the vehicles are merging. Two tasks are performed to effectively determine the merged vehicle group patterns: 1) investigate necessary number of vehicles needed in the MES algorithms, and 2) analyze three decision rules for clustering the vehicle groups. The simulation has shown the accuracy (88.9% approx.) to detect the vehicle groups that merge at a prescribed location.

Citation Download Citation

Kanupriya Salaria, Wiriyanto Darsono, Michael Hinman, Mark Linderman, and Li Bai "Object aggregation using merge-at-a-point algorithm", Proc. SPIE 5434, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2004, (12 April 2004); https://doi.org/10.1117/12.538981

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