Deep Neural Network Compression for Aircraft Collision Avoidance Systems-Reference-Cited by-同舟云学术

Deep Neural Network Compression for Aircraft Collision Avoidance Systems

Published:2019-03 Issue:3 Volume:42 Page:598-608
ISSN:0731-5090
Container-title:Journal of Guidance, Control, and Dynamics
language:en
Short-container-title:Journal of Guidance, Control, and Dynamics

Author:

Julian Kyle D.¹,Kochenderfer Mykel J.¹,Owen Michael P.²

Affiliation:

1. Stanford University, Stanford, California 94305

2. Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, Massachusetts 02421

Funder

Federal Aviation Administration

Publisher

American Institute of Aeronautics and Astronautics (AIAA)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Space and Planetary Science,Aerospace Engineering,Control and Systems Engineering

Link

https://arc.aiaa.org/doi/pdf/10.2514/1.G003724

Reference20 articles.

1. A review of conflict detection and resolution modeling methods

2. Optimized Airborne Collision Avoidance

3. KochenderferM. J.ChryssanthacopoulosJ. P. “Robust Airborne Collision Avoidance Through Dynamic Programming,” Massachusetts Inst. of Technology, Lincoln Lab. Project Rept. ATC-371, Cambridge, MA, 2011.

4. MarstonM.BacaG. “ACAS-Xu Initial Self-Separation Flight Tests,” NASA TR DFRC-E-DAA-TN22968, 2015.

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