Threat detection of people, vehicles, and others as well as person-vehicle interactions (dismounts) of possible malicious intent are difficult problems due to the complexity of the problem space. The wide area motion imagery (WAMI) systems aid analysts to track and identify dismounts, but typically produce an overwhelmingly large amount of information. The large scale data input challenges existing situational awareness algorithms in time complexity and storage requirements. The lack of computationally efficient moving target indicator (MTI) analysis tools has become a bottleneck for utilizing WAMI data in urban surveillance. The Phase I effort has resulted in high-performance computing solutions to handle the large scale data requirements. We obtained promising results to validate our developed approaches. These preliminary yet promising results obtained in the Phase I study clearly demonstrate that IFT’s HPC-MTT, which use the graphics processing unit, multi-core processor, and Hadoop technology, provides innovative and effective on-board and off-board solutions,. Based on the Phase I design and evaluation of our HPC-MTT tool, we believe that it is time to revise, extend, and optimize the Phase I research results using a massively parallel framework with Service-Oriented-Architecture. The proposed high-performance-computing (HPC) enabled multiple-target-tracking (MTT) architecture has tremendous potential for many military applications. The proposed tool can be used to reduce latency to improve E2AT. In C4ISR, the proposed technology can also improve efficiency in the systems that help warfighters process information, such as in the counter-insurgency and counter-IED areas. In addition, there are some relevant Defense Acquisition Programs within DoD, such as Distributed Common Ground Station-Navy (DCGS-N), DCGS-X (Air Force), DCGS Army (DCGS-A) system, and AFRL E2AT and PCPADX.