Application of the sliding innovation filter to unmanned aerial systems

Mohammad AlShabi; S. Andrew Gadsden; Mamdouh El Haj Assad; Bassam Khuwaileh; Stephen Wilkerson

doi:10.1117/12.2587346

12 April 2021 Application of the sliding innovation filter to unmanned aerial systems

Mohammad AlShabi, S. Andrew Gadsden, Mamdouh El Haj Assad, Bassam Khuwaileh, Stephen Wilkerson

Proceedings Volume 11758, Unmanned Systems Technology XXIII; 117580T (2021) https://doi.org/10.1117/12.2587346
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Conference Poster

Abstract

The applications of unmanned aerial systems (UASs) have grown in popularity due to their simplicity and availability. The quality of UAS’s performance depends usually on adding several sensors and controllers that improve accuracy and flight performance. However, this typically increases the overall cost of the system. In this paper, a technique to enhance the performance while maintaining UAS affordability is proposed. This technique involves the use of an estimation strategy to extract hidden information from only a few sensors while improving the quality of the achieved signal. The simulation results of this method show strong performance, and are compared with another well-known estimation method.

Conference Presentation

Citation Download Citation

Mohammad AlShabi, S. Andrew Gadsden, Mamdouh El Haj Assad, Bassam Khuwaileh, and Stephen Wilkerson "Application of the sliding innovation filter to unmanned aerial systems", Proc. SPIE 11758, Unmanned Systems Technology XXIII, 117580T (12 April 2021); https://doi.org/10.1117/12.2587346

ACCESS THE FULL ARTICLE

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