Onboard Wind Velocity Estimation Comparison for Unmanned Aircraft Systems

Matthew B. Rhudy; Yu Gu; Jason N. Gross; Haiyang Chao

doi:10.1109/TAES.2017.2649218

Onboard Wind Velocity Estimation Comparison for Unmanned Aircraft Systems

Matthew B. Rhudy, Yu Gu, Jason N. Gross, Haiyang Chao

Division of Engineering, Business & Computing (Berks)

Research output: Contribution to journal › Article › peer-review

47 Scopus citations

Abstract

This paper presents a novel wind estimation approach, which is compared with existing ideas utilizing different combinations of common aircraft sensors to estimate the wind velocity in real time at the location of an aircraft. These different techniques were evaluated using simulation data as well as two experimental unmanned aircraft flight tests using validation data from a ground weather station. Significant performance advantage was shown of the new filtering technique over the existing approaches.

Original language	English (US)
Article number	7807266
Pages (from-to)	55-66
Number of pages	12
Journal	IEEE Transactions on Aerospace and Electronic Systems
Volume	53
Issue number	1
DOIs	https://doi.org/10.1109/TAES.2017.2649218
State	Published - Feb 2017

All Science Journal Classification (ASJC) codes

Aerospace Engineering
Electrical and Electronic Engineering

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10.1109/TAES.2017.2649218

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title = "Onboard Wind Velocity Estimation Comparison for Unmanned Aircraft Systems",

abstract = "This paper presents a novel wind estimation approach, which is compared with existing ideas utilizing different combinations of common aircraft sensors to estimate the wind velocity in real time at the location of an aircraft. These different techniques were evaluated using simulation data as well as two experimental unmanned aircraft flight tests using validation data from a ground weather station. Significant performance advantage was shown of the new filtering technique over the existing approaches.",

author = "Rhudy, {Matthew B.} and Yu Gu and Gross, {Jason N.} and Haiyang Chao",

note = "Funding Information: This work was supported in part by the National Aeronautics and Space Administration under Grant NNX13AB74A and Grant NNX14AF55A. Publisher Copyright: {\textcopyright} 1965-2011 IEEE.",

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AB - This paper presents a novel wind estimation approach, which is compared with existing ideas utilizing different combinations of common aircraft sensors to estimate the wind velocity in real time at the location of an aircraft. These different techniques were evaluated using simulation data as well as two experimental unmanned aircraft flight tests using validation data from a ground weather station. Significant performance advantage was shown of the new filtering technique over the existing approaches.

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Onboard Wind Velocity Estimation Comparison for Unmanned Aircraft Systems

Abstract

All Science Journal Classification (ASJC) codes

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