Fuzzy Adaptive Attitude Estimation for a Fixed-Wing UAV with a Virtual SSA Sensor During a GPS Outage

This paper proposes a novel robust attitude estimation algorithm for a small unmanned aerial vehicle (UAV) in the absence of GPS measurements. A synthetic sideslip angle (SSA) measurement formulated for use under the zero-angle assumption is newly proposed for a UAV without angle-of-attack (AOA)/SSA sensors to enhance the state estimation performance during a GPS outage. In addition, the nongravitational acceleration is estimated using the proposed Kalman filter and is then subtracted from the raw acceleration to yield a reliable gravity estimate. Then, a fuzzy-logic-aided adaptive measurement covariance matching algorithm is devised to adaptively reduce the weight given to disturbed acceleration and magnetic field measurements in the attitude estimation, yielding the fuzzy adaptive error-state Kalman filter (FAESKF) algorithm. Experimental flight results demonstrate that the proposed FAESKF algorithm achieves a remarkable improvement in attitude estimation compared to the conventional algorithm.

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Work Title Fuzzy Adaptive Attitude Estimation for a Fixed-Wing UAV with a Virtual SSA Sensor During a GPS Outage
Access
Open Access
Creators
  1. Wonkeun Youn
  2. Matthew B. Rhudy
  3. Am Cho
  4. Hyun Myung
License In Copyright (Rights Reserved)
Work Type Article
Publisher
  1. Institute of Electrical and Electronics Engineers (IEEE)
Publication Date February 1, 2020
Publisher Identifier (DOI)
  1. 10.1109/JSEN.2019.2947489
Source
  1. IEEE Sensors Journal
Deposited September 09, 2021

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  • Added Creator Am Cho
  • Added Creator Hyun Myung
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