Innovative Automated Support of Data Analysis for Emerging Missile Defense System (MDS) Capabilities

RT&L FOCUS AREA(S): Autonomy; Artificial Intelligence/ Machine Learning TECHNOLOGY AREA(S): Information Systems OBJECTIVE: Develop innovative data science technologies to advance data analysis within a missile defense digital architecture. DESCRIPTION: This topic seeks innovative complex data science methods that go beyond traditional data analysis. The focus should be on the creation of efficient and cost-effective technologies to analyze large data sets derived from missile defense modeling and simulation (M&S) results. As the MDS evolves from primarily terrestrial Radio Frequency (RF) sensor architectures to incorporating many overhead Electro-Optical/Infrared (EO/IR) sensors to support the hypersonic missile defense mission, as well as new sensors and data taps to assure that missile defense systems are cyber secure, our ability to correlate and analyze the data from these sensors must evolve both at a system and system of systems level. Current analysis methods have difficulties with this predicted volume, variety, veracity, and velocity of data, especially when applied to data science, digital engineering and engineering simulations. Additionally, performance assessment studies using all-digital simulations generate nearly two petabytes of data annually. Data science technology areas of interest include, but are not limited to: algorithms, software tools, and turnkey solutions for complex analysis techniques (i.e. transformative, predictive and learning) that employ learning models (i.e. supervised, unsupervised and reinforcement styles) with different employment approaches (scheduling and sequencing). PHASE I: Design and develop innovative solutions, methods, and concepts to advance data science methods as it pertains to the analysis and results of data science, digital engineering, and engineering simulations in a cloud-like environment. The solutions should capture the key areas for new development, suggest appropriate methods and technologies to minimize the time intensive processes, and incorporate new technologies researched during the design and development. PHASE II: Complete a detailed prototype design incorporating government performance requirements. Coordinate with the government during prototype design and development to ensure that the delivered products will be relevant to an ongoing missile defense architecture and data types and structures. PHASE III DUAL USE APPLICATIONS: Scale-up the capability from the prototype utilizing the new technologies developed in Phase II into a mature, full scale, field-able capability. Work with missile defense integrators to integrate the technology into a missile defense system level test-bed and test in a relevant environment. REFERENCES: 1. G. Deshpande, C. Arora and G. Ruhe, "Data-Driven Elicitation and Optimization of Dependencies between Requirements," 2019 IEEE 27th International Requirements Engineering Conference (RE), Jeju Island, Korea (South), 2019, pp. 416-421, doi: 10.1109/RE.2019.00055. 1. https://ieeexplore.ieee.org/document/8920671 ; 2. C. Wang, L. Ma, R. Li, T. S. Durrani and H. Zhang, "Exploring Trajectory Prediction Through Machine Learning Methods," in IEEE Access, vol. 7, pp. 101441-101452, 2019, doi: 10.1109/ACCESS.2019.2929430. https://ieeexplore.ieee.org/document/8766820 ; 3. R. M. M. Vallim, A. C. P. L. F. de Carvalho and J. Gama, "Data Stream Mining Algorithms for Building Decision Models in a Computer Role-Playing Game Simulation," 2010 Brazilian Symposium on Games and Digital Entertainment, Florianopolis, 2010, pp. 108-116, doi: 10.1109/SBGAMES.2010.14. https://ieeexplore.ieee.org/document/5772278 ; 4. Y. T. Demey and M. Wolff, "SIMISS: A Model-Based Searching Strategy for Inventory Management Systems," in IEEE Internet of Things Journal, vol. 4, no. 1, pp. 172-182, Feb. 2017, doi: 10.1109/JIOT.2016.2638023. https://ieeexplore.ieee.org/document/7778999