TY - JOUR
T1 - Fuzzy Adaptive Attitude Estimation for a Fixed-Wing UAV with a Virtual SSA Sensor during a GPS Outage
AU - Youn, Wonkeun
AU - Rhudy, Matthew B.
AU - Cho, Am
AU - Myung, Hyun
N1 - Funding Information:
Manuscript received September 1, 2019; revised October 8, 2019; accepted October 8, 2019. Date of publication October 15, 2019; date of current version January 17, 2020. This work was supported in part by the Unmanned Vehicles Advanced Core Technology Research and Development Program through the National Research Foundation of Korea (NRF), in part by the Unmanned Vehicle Advanced Research Center (UVARC), Ministry of Science and ICT, South Korea, under Grant NRF-2017M1B3A2A01049995, in part by the First-Mover Program for Accelerating Disruptive Technology Development under Grant NRF-2018M3C1B9088328, and in part by BK21+. The associate editor coordinating the review of this article and approving it for publication was Dr. You Li. (Corresponding author: Hyun Myung.) W. Youn is with the UAV System Division, Aeronautics Research and Development Head Office, Korea Aerospace Research Institute, Daejeon 34133, South Korea, and also with the Urban Robotics Laboratory (URL), Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, South Korea (e-mail: [email protected]).
Publisher Copyright:
© 2001-2012 IEEE.
PY - 2020/2/1
Y1 - 2020/2/1
N2 - 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.
AB - 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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U2 - 10.1109/JSEN.2019.2947489
DO - 10.1109/JSEN.2019.2947489
M3 - Article
AN - SCOPUS:85078531522
SN - 1530-437X
VL - 20
SP - 1456
EP - 1472
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 3
M1 - 8869915
ER -