C4I and Software Intensive Systems Test
Abstract
The C4T Threat Submarine Modeling Validation project transitioned advanced modeling and simulation technology capabilities to the Naval Undersea Warfare Center (NUWC) Weapons Analysis Facility (WAF) validated (by COTF and DOT&E) modeling capabilities that resulted in over $150 million of saving by reducing the number of live in-water runs required for the MK 48 Heavy Torpedo. These technologies will be instrumental to all future next generation torpedo developments as well as current torpedo system upgrades. With these advanced M&S capabilities we can now finally assess performance of torpedoes in all underwater bathometry (e.g. deep, shallow, and varying ocean ecology). The C4T MultiVariate Data Workbench (MVDW) is transitioning to the US Army Fort Sills Test Directorate providing advanced AI/ML technologies to support near real-time data collection and validation for the US Army indirect fire doctrine. Data collection includes structured and unstructured datasets which currently requires multiple days to validate after collection and often resulting in retesting cycles as anomalies are not recognized during execution. MVDW will provide these answers after the completion of each test day. This exciting technology product is being used to support US Army Bold Quest 2022. The C4T Multivariate Algorithms for Optimized Test Heuristics and Real-time Analysis (MAOTHRA) is transitioning to the Redstone Test Center ATEC providing advanced statistical analytic techniques in a parallel processing computing environment to automatically calibrate cameras (low-cost, high-speed) to support generation of TSPI on weapon systems test events, resulting in cost savings from existing high-cost cameras with lengthy (hours) calibration techniques to low-cost cameras that are calibrated within minutes. MAORTHRA AI/ML techniques for analysis of large multivariant data sets to provide valuable insights from time-series weapon systems sensor are presently supporting the US Army Project Convergence 2022. The C4T project continued the development of several big data analytics (BDA) efforts implementing artificial intelligence/ machine learning (AI/ML) techniques for multi-variant time series sensor datasets, unstructured dataset analytics (audio, video, and imagery), and advanced visualizations of large T&E datasets. These efforts include: traditional statistical and machine learning/artificial intelligence (ML/AI) techniques to deal with massive complex datasets; the software execution has been focused on the use of containerized microservices architecture for ease of technology transfer across all T&E organizations. Common technologies across C4T project also supports advanced data synchronization and fusion frameworks to automate development of assessment metrics and to quickly recall synchronized segments from large T&E datasets (e.g., multivariate time series, audio, video, and imagery. Lastly, C4T project is creating advanced visualization techniques; to support the presentation of information by abstracting data into particles to optimally exploit current vision and neuroscience research. This allows the T&E analyst to visualize anomalies, trends, patterns, and failure conditions found across the entirety of the T&E dataset and not be focused on an individual dataset. These technologies are being developed to support test and evaluation of future warfighter C4I and Software Intensive Systems (4th and 5th generation military platforms).
Document Details
- Document Type
- Accomplishment
- Publication Date
- Oct 01, 2024
- Source ID
- 1ffdf0803bcf97cae639c52e2a964b67