Aircraft Classification Using Radar from Small Unmanned Aerial Systems for Scalable Traffic Management Emergency Response Operations
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Chester V Dolph↗
Engineering topics
George N Szatkowski
Publications and source records attributed to George N Szatkowski.
TPSAS-NF1676L-19165-DND
To fully leverage the advantages of composites in new aerospace vehicles and applications requires continuous investigation of novel technologies beyond the current state-of-the-art. An open circuit resonant sensor has been developed for the purpose of in-situ damage detection and diagnostics in non-conductive and conductive aerospace composite materials.
Kenneth L Dudley↗
TPSAS-NF1676L-12617-DND
Develop innovative lightning strike mitigation technologies to minimize flight safety risks from electromagnetic environmental hazards. (Lightning & HIRF).
George N Szatkowski↗
Digital Information Platform (DIP) Overview
On March 24, 2021, NASA’s Digital Information Platform (DIP) released a Request for Information (RFI) to the aviation community. The RFI was intended to request two things: 1) DIP concept input such as current challenges and needs for data and services and 2) Interest in participating in collaborative demonstrations. On July 29, 2021, DIP team will meet with Airlines for America (A4A) to share a summary of information received from the RFI responses. The objective of the meeting is to follow up with respondents with takeaways the DIP team identified that will inform the concept and demonstration plans. The forum will give participants an opportunity to add on and provide feedback to the summary. The meeting will also describe the roadmap for the Collaborative Demonstrations and next steps for partner engagement.
Digital Information Platform (DIP) RFI summary↗
Aerial Object Trajectory Classification by Training on Flight Controller Data and Testing on RADAR Generated Tracks
Onboard collision avoidance is needed to enable safe, autonomous flight operations for NASA projects such as Advanced Air Mobility (AAM), as well as many commercial applications. Real-time aerial object classification will improve onboard collision avoidance algorithm decision making and may reduce unnecessary activation of avoidance systems. This work trains an aircraft trajectory classifier using trajectories from flight controller logs and tests the classifier using RADAR collected trajectories during air to air experiments and ground to air experiments. In contrast to RADAR data, these flight controller logs are relatively abundant, which makes the possibility of substituting flight data for RADAR data an attractive, cost-effective option. The SVM model developed in this work achieved a 79.7% classification accuracy on the first second of radar trajectories of GA, multirotor sUAS, and fixed wing sUAS. Findings from this work indicate that it is feasible to classify sensor collected trajectories using a classifier trained on flight controller data.
Henry Holbrook↗