Ms. Ellen Hein Profile

Ellen Hein

at BAE Systems

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 12 June 2023 Presentation

Improving flight planning robustness through NOTAM Q-code error correction (Conference Presentation)

Kalyan Vaidyanathan, Ty Danet, Ellen Hein

Proceedings Volume 12538, 125380A (2023) https://doi.org/10.1117/12.2664018

KEYWORDS: Machine learning, Data modeling

Read Abstract +

One of the inputs to a flight planning system is human-generated Notice to Airmen (NOTAM) entries that are used to alert flight crews of potential hazards that may be encountered when flying a specific mission. The text descriptions contained within the NOTAM are heavily abbreviated domain-specific free text, not standardized, and can vary widely depending on how the issuer chooses to describe the situation. Automated flight planning systems (autoplanners) do not parse the text description to determine accuracy or viability. Instead, a 4-letter code (known as a Q-Code) is entered and recorded by the human issuer to aid in interpreting the NOTAM contents. If Q-Codes are improperly entered, wrong, incomplete, or not specific to the issue being described, NOTAMs may be erroneously interpreted and the autoplanner may generate sub-optimal flight plans. As a solution to the problem, we developed machine-learning based text classification models where the inputs are NOTAM text descriptions and the outputs are predicted Q-codes for each text description. Such a solution would make autoplanner systems more robust by verifying and correcting incorrect NOTAMs automatically.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years