SBIR-STTR Award

Using integrated teams of Operational Analysts experienced with Naval aircrew training and Software Engineers experienced in developing dynamic analysis and reporting tools for aviation, a pragmatic and scalable approach to aircrew performance assessment and proficiency is proposed. Initial techniques to incorporate data fusion of disparate data sources are summarized from past experience and existing software solutions. These techniques can be applied to aircrew performance assessment to reduce instructor work-load while also increasing the speed and accuracy of assessment. A process outline to decompose training scenarios in a manner that supports automation, human-in-the-loop techniques, and advanced data science is presented for further refinement during the research. The methodologies proposed for research all include the ability to scale across multiple platforms, training devices, training missions, and aircrew configurations through innovative application of modern programming paradigms. The end result is a design realizing this innovation in a user-oriented toolset supporting actionable feedback in a performance assessment and proficiency system.

Benefit:
The preparation of tactically and technically proficient aircrew capable of conducting Major Combatant Operations (MCO) in support of the United States National Command Authority (NCA) is the end-sate objective of the Naval Air Force. Many high-end efficiencies are demonstrated by the NAE solution currently in place, however, the low-end tasks of collecting data for Individual Aircrew Performance Assessment and Squadron/Detachment Performance Assessment to support Immediate Superiors in Command (ISIC) validation and completion of CB T&R training requirements is not as refined. Few, if any, commercially-available debriefing system vendors have made any significant inroads into collecting, storing and presenting information via commonly-accepted applied data science techniques applicable to the desired end-state solution. With aircraft and weapon systems increasing in complexity, limited flight hour funding, range and airspace constraints, increased operational tempo, aircrew and airframe shortages, and a challenging data collection landscape all conspiring to make the problem of performance assessment more difficult, BGI will employ proven and effective data science methodologies that feed accurate and objective data into the NAE process for both Individual Aircrew Performance Assessment and Squadron/Detachment Performance Assessment. With automated collection techniques operating on correlated data, an instructor is supported with indicators of performance supported by objective evidence.

Keywords:
Analytics, Analytics, Visualization, Risk Management Framework, Human In The Loop, aircrew proficiency, data fusion, Performance Measurement, Data science

Todays combat environment is a complex orchestration of multiple systems operated by people requiring a high degree of training to be effective. Training is both difficult and time consuming, with high demand on a small number of skilled instructors. There is a need to improve the quality of training, provide objective performance assessments, reduce the instructor workload, and increase the efficiency of debrief. Through the application of modern data processing platforms and Data Science it is possible to automate data retrieval and processing techniques that are currently done manually. Through decomposition of Navy training doctrine, a set of competencies is defined with associated performance measures. These measurements are then composed to depict the overall effectiveness of the warfighter, categorized by core competencies, and presented to an instructor for final performance assessment. These techniques also open potential for advanced analytics, sophisticated training approaches, tactics development, and data-driven warfighter training. This is accomplished through extending existing Navy training systems to provide a cost-effective approach to accomplish the goals.

Benefit:
The proposed approaches are applicable to any area where training takes place, including other branches of the Department of Defense and areas outside aviation. The specific work being performed integrates with the Next Generation Threat System (NGTS). As a plugin extension of capability to an existing product, the developed software can be easily integrated on existing NGTS installation. This opens an extremely broad range of applications. The completed software application will provide objective performance assessments to training currently performed largely through subjective assessments. In providing objective evidence, the software also automates many of the manual steps currently performed during training. This improves the efficiency of debrief and reduces the workload on instructors and aircrew, who can then spend more time on improving their effectiveness.

Keywords:
Training, Performance Assessment, Effects Chain, Next Generation Threat System (NGTS), Data science, Machine Learning, Warfighter Effectiveness