NDLI: System Identification of UAV Based on EWC-LMS

Content Provider	IEEE Xplore Digital Library
Author	Zijing Zhou Jinchun Hu Shiqian Liu Badong Chen
Copyright Year	2006
Description	Author affiliation: Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing (Zijing Zhou; Jinchun Hu; Shiqian Liu; Badong Chen)
Abstract	Mean-squared error (MES) can at best provide biased solution in the presence of additive disturbances (both correlated and white) on the input and the output signals. However, in the system identification of unmanned aerial vehicle (UAV), typically, in the takeoff, the UAV suffers serious disturbance e.g. wind gusts, turbulence, ground influence and sampling noise. For EWC, we can get the unbiased parameter estimation in white noise, and the system identification in this paper prove the noise rejection ability of EWC. EWC (error whitening criterion) was first put forward by Mr. Rao in 2002 and applied in filtering, and further developed theoretically. But EWC has not been applied in practical problems yet. This paper applies EWC in the system identification of real self-developed UAV's takeoff motion, and compares the result of EWC-LMS with traditional LMS (least mean square) and TLS (total least square). The result shows that in comparison with traditional LMS and TLS, EWC-LMS improve the performance significantly
Starting Page	721
Ending Page	726
File Size	993167
Page Count	6
File Format	PDF
ISBN	7302139229
DOI	10.1109/CESA.2006.4281747
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-10-04
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	IMACS
Subject Keyword	Parameter estimation Error whitening criterion UAV Application software Least squares approximation Computer science LMS White noise Computer errors Systems engineering and theory Unmanned aerial vehicles System identification Testing
Content Type	Text
Resource Type	Article

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