Krause, Aaron - (661) 277-0794

The objective of this project is to research the comparable effectiveness and utility of assessing flight autonomy capabilities on small unmanned aircraft system (sUAS) surrogates instead of larger, less cost-effective surrogates more closely related to a non-existent target platform. Only flying autonomies on larger, more similar surrogates may be limiting autonomy development. Larger, more similar surrogates are costly and may not be necessary to understand some portion of an autonomy’s capability. sUAS are significantly less expensive to operate and maintain than high-performing surrogates (e.g., F-16, XQ-58) and are far easier to replace. Flight test using a sUAS is also significantly easier to execute and allows for a higher sortie rate, thereby generating more autonomy flight data. Autonomy algorithms can be developed and tested using sUAS surrogates as a part of a larger build up process, reducing risk and development time. sUAS may be a more cost-effective bridge between simulation and larger surrogates, and their comparable effectiveness and utility needs to be better understood. The end goal of this topic is to show where sUAS are equivalent to larger surrogates for assessing autonomy capabilities intended for a larger non-existent target platform.

Jessen, Jeff - (661) 275-1284

The objective of this project is to research options for implementing a small unmanned aerial systems (sUAS) flight control system (FLCS) capable of supporting different dynamics through a model following algorithm (aka micro-VISTA), to support rate control input and output to control-surface actuators. The proposed effort is focused on researching the technologies needed to provide an interface for lower-level commands (LLC) between autonomy algorithms and a sUAS simulating the dynamics of another aircraft. The intended UAS FLCS should permit autonomy algorithms to use LLCs to fly the aircraft through the allowable flight envelope but should intercede to prevent loss of control in cases where the aircraft would exceed limits. Autonomy algorithms can be developed and tested using this capability with reduced risk and development time.

Poulson, Robert - (661) 277-4031

As part of the Air Force Test Center, the 412th Test Wing located at Edwards AFB plans, conducts, analyzes, and reports on all flight and ground testing of aircraft, weapons systems, software and components as well as modeling and simulation for the USAF. Statistically defensible testing is an inherent component of developing and conducting quality tests and is infused throughout the testing and reporting process. Opportunities for current and future research and development of statistical methods include the following topics:

Develop experimental designs to optimize flight time and minimize constraint on randomization.
Use Bayesian methods to build predictive models and develop posterior distributions of complex, hierarchical flight test systems.
Research tail sampling to better understand inferences on percentiles including order and extreme value statistics.
Develop methods for modeling time series and spatial statistics related to flight tests.
Develop categorical data analysis methods to improve testing of human interaction with flight test systems. Such as survey analysis, testing of audio and communication systems, and human interface with flight systems.
Build supporting software in R and/or Python packages that would facilitate implementation of statistical methods into flight testing.

Brownlow, James - 661-277-4843

Aircraft verbal communications are frequently noisy and/or incomplete. This research effort is to develop an AI/ML approach to clarify / complete verbal or message transmissions. This entails prediction of missing words prediction of correct words for incomplete or 'non' words and characterization of error rates in aircraft communications

Brownlow, James - 661-277-4843

Complex Aircraft systems require reliability and maintainability analyses that support frequent aircraft-system updates, reliability growth and the analysis of software-induced failures. Interest is in robust analysis/modeling techniques / procedures that support these objectives.

Brownlow, James - 661-277-4843

The advent of large data sets and digital engineering opens the door to the use of Machine Learning/Artificial Intelligence in modeling and analyses of flight test data. This investigation is designed to develop analysis and models from large data sets. Interest is in models developed for both real-time and flight-test analysis applications. Such models form part of the digital engineering efforts. Dimension-reducing, Bayesian, multiple time series are some examples of approaches that may be used.

Upperman, Gary - (661) 277-1464

The 771st Test Squadron performs developmental test on electronic warfare (EW) systems. We have been developing a machine learning (ML) tool in Python to assist with data analysis. The goal is to be able to train a model that will perform "site-matching" (data association/reconciliation between a radar warning receiver and truth data from an electronic combat range), and then use that model to automatically perform this analysis step on new data, accelerating the data reduction process.
Preliminary testing demonstrated great success, but further testing with more complex data sets has yielded mixed results. Our desired research program would investigate new ML algorithms that would be more successful in EW data association. Currently the algorithms used have been limited to the scikit-learn python package. We also would like to investigate using unsupervised machine learning to perform clustering on the data and attempt to identify previously unrecognized RF signals. The goal is to have an algorithm that will predict new data-sets with 95% accuracy as compared to our traditional manual analysis techniques.

Stephens, Shawn - 661-277-4649

The UASF Test Pilot School is the world’s premier institution for flight test education. We teach flight test techniques and flight test planning methods for both piloted and unpiloted fixed wing vehicles. We are seeking highly-qualified and motivated individuals for collaboration in the advancement of current state-of-the-art methodology in specific research and teaching areas while enhancing the capabilities of tools employed by USAF TPS to support test and evaluation education. Opportunities for focused research include the following topics:

• Advancement of our ability to teach complex topics to our students in minimal time using modern engineering education techniques, including the flipped classroom, direct application of theoretical concepts to student flight tests, simulation exercises that reinforce academic concepts, and educational tools for use in classroom and homework exercises.

• UAS curriculum evaluation and development to include specific flight test techniques for UAS platforms. USAF TPS is building a UAS flight testing curriculum and welcomes input for review and evaluation from others. TPS is also researching techniques to increase pilot/operator effectiveness in the presence of multiple seconds of time delay between a ground station and aircraft. TPS is also interested in instrumentation of Group I, II, and III UAS.

• Human interaction with complex systems to include Handling Qualities of complex control systems to include adaptive/robust systems and the prediction of new operational flight envelopes when robust control is actively accounting for uncertainties.

TPS has state of the art flight simulation capabilities and rapid prototyping of control going from simulation model to flight within the same day. TPS has unique access to multiple aircraft, and staff capable of conducting advanced/complex flight testing.