C4I and Software Intensive Systems Test
Abstract
The C4T project completed development of AI technologies in multiple areas of “Big Data” rapid analytics of large structured and unstructured datasets in support of F-35 Test and Evaluation (T&E). This includes developing technology that employs unsupervised machine learning to assist humans to analyze, extract, & manage actionable knowledge from many varied large data sets (not just F-35); using Human-Like reasoning to identify insights from structured and unstructured data; enable distributed testers to use shared knowledge to identify critical test information. This effort transitioned to the JSF Joint Program Office and Edwards AFB Test Pilot School. The C4T project completed development of M&S technologies to support real-time assessments of torpedo performance in complex undersea environments, specifically for shallow water (<50 meters). These technologies provide an acoustic propagation model for both narrow and broad band, of sufficient fidelity to be used for the next generation of torpedo development as well as testing torpedo performance in various maritime tactical environments that cannot be assessed with live in-water testing. The model includes a real-time simulation/emulation system for design and testing the next generation of torpedo sonar systems in multiple bathometry, biological and threat environments. This effort transitioned to naval undersea weapons and unmanned vehicle programs. The C4T project completed development of a network M&S to achieve configuration optimization of test support networks. Technologies included planning expeditionary tests, managing bandwidth and spectrum contention with a networked system under test, managing power consumption providing a continuous re-planning capability. These technologies will address deficiencies in Army Operational Test (OT) for network-enabled technologies to support the Operational Test Command at Ft. Hood, Texas. The C4T project initiated 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 visualization of large T&E datasets. These efforts include: traditional statistical and machine learning techniques to deal with massive complex datasets; containerized microservices architecture to support systemic analysis utilizing advanced analytics (ML/AI algorithms); advanced data synchronization and fusion framework and services allowing users to correlate and assess multivariate data types for operational test analysis; cloud-based microservices framework to speed synchronization of text format outputs by ML models and accompanying metrics on precision and recall for audio, video, and imagery large T&E datasets; collection, analysis and visualization of multi-variate data across system lifecycle; advanced visualization techniques; browser-based visualization technology to easily ingest massive data sets from multiple sources, store data locally for enrichment purposes, and export data and analytical products; and browser-based client-server Data Observatory visualization technology that reinvents the presentation of information by abstracting data into particles to optimally exploit current vision and neuroscience research allowing the analyst to receive the most information without focusing on each piece individually. 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, 2023
- Source ID
- a7404220f252b63ae789506364944575