The Post-9/11 GI Bill overhauled the educational benefits available to military veterans. Additionally, the Yellow Ribbon Program makes private institutions more affordable to veterans. Consequently, more veterans are seeking undergraduate degrees, and many schools are seeing significant numbers of veterans for the first time. The National Science Foundation (NSF) has recognized that veteran engineers may address shortages in the engineering workforce and has funded projects to develop models for helping veterans make the transition from active duty to successful student. This special session provides a forum for several awardees to describe their work and to engage a larger audience in discussions about engineering education for veterans. The goals for this session are to raise awareness in the engineering education community about the issues facing veteran engineering students, identify issues commonly encountered as veterans transition from service to the classroom, promote the dissemination of results from NSF-supported efforts to support veterans in engineering programs, provide a forum for sharing best practices related to the successful transition of a veteran from the military to engineering programs, and establish relationships between schools with the shared interest of serving military veteran students.
Having a better motion model in the state estimator is one way to improve target tracking performance. Since the motion model of the target is not known a priori, either robust modeling techniques or adaptive modeling techniques are required. The neural extended Kalman filter is a technique that learns unmodeled dynamics while performing state estimation in the feedback loop of a control system. This coupled system performs the standard estimation of the states of the plant while estimating a function to approximate the difference between the given state-coupling function model and the behavior of the true plant dynamics. At each sample step, this new model is added to the existing model to improve the state estimate. The neural extended Kalman filter has also been investigated as a target tracking estimation routine. Implementation issues for this adaptive modeling technique, including neural network training parameters, were investigated and an analysis was made of the quality of performance that the technique can have for tracking maneuvering targets.
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