SBIR-STTR Award

The objective of this proposal is to design a software solution that consolidates performance data from multiple sources to ensure that data collection and assessment processes are accurate, efficient, and effective. The designed solution will leverage the work already conducted by NAVAIRs Training Analysis Project (TAP) regarding F/A-18 air warfare. In doing so, the software will adhere to training best practices and align with the performance metrics that TAP has already identified as being critical to mission success in Component Commander plans. Since TAPs processes were designed to be scalable, this software will provide a methodology for the NAE to duplicate the entire TAP process for other platforms and mission sets in a manner that is streamlined and efficient. Artificial intelligence will be applied to enable automated and semi-automated methods for collection of data, assessment of performance, and reporting of training deficiencies. This improves the speed and effectiveness of the feedback to the trainee and the NAE leadership. The software will be designed to reside on hardware that is already resident at Fleet training locations (to reduce the physical footprint) and will incorporate an intuitive user interface (to minimize the need for additional aircrew training).

Benefit:
The proposed software solution will provide the ability to automate or semi-automate the collection, assessment, and reporting of performance metrics that directly link to the requirements for mission success. Automated or semi-automated data collection will improve the efficiency of data collection for the TAP team, which provides for either a reduction in cost or an increase in activity (given the same investment). As well, it provides Fleet aircrew with the access to objective, quantifiable data at the same level of effectiveness as the Training Analysis Project (TAP) data collectors today given the limitations of the current collection process. This improves the quality of the training and ultimately increases warfighter effectiveness. Finally, this system provides a means for standardizing the aircrew performance data reporting process, which improves the feedback loop to NAE decision makers.

Keywords:
Performance reporting, Performance reporting, Increased Warfighter Effectiveness, Artificial Intelligence, Performance standards, Effects Chain, automated data collection, Performance Assessment, Debrief

The objective of this SBIR Phase II is to apply the research that was conducted during SBIR Phase I to develop a software solution that consolidates performance data from multiple sources to ensure that data collection and assessment processes are accurate, efficient, and effective. The solution leverages the processes used by NAVAIRs Training Analysis Project (TAP) and provides a tool that can be used by both aircrew and data collectors to conduct performance assessment. The design and function of the software adhere to training best practices and align with the performance metrics that TAP has already identified as being critical to mission success in Component Commander plans. Although Phase II will focus on F/A-18 AW and STW, the software is scalable in that it will provide a methodology for the NAE to duplicate the entire TAP process for other platforms and mission sets in a manner that is streamlined and efficient. The software uses Artificial intelligence to enable automated and semi-automated methods for collection of data, assessment of performance, and reporting of training deficiencies. The software is intended to improve the speed and effectiveness of the feedback to the trainee and the NAE leadership.

Benefit:
The software solution provides the ability to automate or semi-automate the collection, reconstruction, analysis, assessment, and reporting of performance metrics that directly link to the requirements for mission success. This improves the efficiency of the process developed by the TAP team, which provides for either a reduction in cost or an increase in activity (given the same investment). As well, it provides Fleet aircrew with access to objective, quantifiable data that is linked to operational requirements. This improves the quality of the training and ultimately increases warfighter effectiveness. Finally, the approach taken by the software provides a means for automating the aircrew performance data reporting process, which improves the feedback loop to NAE decision makers.

Keywords:
Consolidated Debrief, Automated Performance Assessment, Data Collection, Artificial Intelligence, Proficiency, mission planning, Machine Learning, RAPID Software