Adaptive Dual Wavelet Threshold Denoising Function Combined with Allan Variance for Tuning FOG-SINS Filter

doi:10.1007/s12204-020-2208-x

Journal of Shanghai Jiao Tong University(Science) ›› 2020, Vol. 25 ›› Issue (4): 434-440.doi: 10.1007/s12204-020-2208-x

Adaptive Dual Wavelet Threshold Denoising Function Combined with Allan Variance for Tuning FOG-SINS Filter

BESSAAD Nassim, BAO Qilian (鲍其莲), SUN Shuodong (孙朔冬), DU Yuding (杜雨丁), LIU Lin (刘林), HASSAN Mahmood Ul

(1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;
2. Shanghai Aerospace Control Technology Institute, Shanghai 201109, China)

出版日期:2020-08-28 发布日期:2020-07-29
通讯作者: BAO Qilian (鲍其莲) E-mail:qlbao@sjtu.edu.cn

Adaptive Dual Wavelet Threshold Denoising Function Combined with Allan Variance for Tuning FOG-SINS Filter

BESSAAD Nassim, BAO Qilian (鲍其莲), SUN Shuodong (孙朔冬), DU Yuding (杜雨丁), LIU Lin (刘林), HASSAN Mahmood Ul

(1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;
2. Shanghai Aerospace Control Technology Institute, Shanghai 201109, China)

Online:2020-08-28 Published:2020-07-29
Contact: BAO Qilian (鲍其莲) E-mail:qlbao@sjtu.edu.cn

摘要/Abstract

摘要： Allan variance (AV) stochastic process identification method for inertial sensors has successfully combined
the wavelet transform denoising scheme. However, the latter usually employs a traditional hard threshold
or soft threshold that presents some mathematical problems. An adaptive dual threshold for discrete wavelet
transform (DWT) denoising function overcomes the disadvantages of traditional approaches. Assume that two
thresholds for noise and signal and special fuzzy evaluation function for the signal with range between the two
thresholds assure continuity and overcome previous difficulties. On the basis of AV, an application for strap-down
inertial navigation system (SINS) stochastic model extraction assures more efficient tuning of the augmented 21-
state improved exact modeling Kalman filter (IEMKF) states. The experimental results show that the proposed
algorithm is superior in denoising performance. Furthermore, the improved filter estimation of navigation solution
is better than that of conventional Kalman filter (CKF).

关键词: Allan variance (AV), discrete wavelet transform (DWT), adaptive dual threshold, fiber optic gyroscope
(FOG), strap-down inertial navigation system (SINS), exact modeling filter

Abstract: Allan variance (AV) stochastic process identification method for inertial sensors has successfully combined
the wavelet transform denoising scheme. However, the latter usually employs a traditional hard threshold
or soft threshold that presents some mathematical problems. An adaptive dual threshold for discrete wavelet
transform (DWT) denoising function overcomes the disadvantages of traditional approaches. Assume that two
thresholds for noise and signal and special fuzzy evaluation function for the signal with range between the two
thresholds assure continuity and overcome previous difficulties. On the basis of AV, an application for strap-down
inertial navigation system (SINS) stochastic model extraction assures more efficient tuning of the augmented 21-
state improved exact modeling Kalman filter (IEMKF) states. The experimental results show that the proposed
algorithm is superior in denoising performance. Furthermore, the improved filter estimation of navigation solution
is better than that of conventional Kalman filter (CKF).

Key words: Allan variance (AV), discrete wavelet transform (DWT), adaptive dual threshold, fiber optic gyroscope
(FOG), strap-down inertial navigation system (SINS), exact modeling filter

中图分类号:

V 249.32

BESSAAD Nassim, BAO Qilian, SUN Shuodong, DU Yuding, LIU Lin, HASSAN Mahmood Ul . Adaptive Dual Wavelet Threshold Denoising Function Combined with Allan Variance for Tuning FOG-SINS Filter[J]. Journal of Shanghai Jiao Tong University(Science), 2020, 25(4): 434-440.

参考文献 15

[1]	GREWAL M S, ANDREWS A P. Kalman filtering theory and practice using MATLAB [M]. 3rd ed. New York, USA: Wiley, 2008: 1-172.
[2]	TITTERTON D, WESTON J. Strapdown inertial navigation technology [M]. 2nd ed. Stevenage, UK: Institution of Engineering and Technology, 2004: 24-47.
[3]	SAVAGE P G. Strapdown analytics: Part 2 [M]. Maple Plain, USA: Strapdown Associates, 2000: 15-140.
[4]	QUINCHIA A G, FALCO G, FALLETTI E, et al. A comparison between different error modeling of MEMS applied to GPS/INS integrated systems [J]. Sensors,2013, 13(8): 9549-9588.
[5]	Gyro and Accelerometer Panel of the IEEE Aerospace and Electronic Systems Society. IEEE standard specification format guide and test procedure for linear,single-axis, non-gyroscopic accelerometers: IEEE std 1293-1998 (R2008) [S]. New York, USA: IEEE, 1999.
[6]	GU S S, LIU J Y, ZENG Q H, et al. A Kalman filter algorithm based on exact modeling for FOG GPS/SINS integration [J]. Optik, 2014, 125(14): 3476-3481.
[7]	WOODMAN O J. An introduction to inertial navigation:UCAM-CL-TR-696 [R]. Cambridge, UK: Computer Laboratory, University of Cambridge, 2007.
[8]	SABAT S L, GIRIBABU N, NAYAK J, et al. Characterization of fiber optics gyro and noise compensation using discrete wavelet transform [C]//Second International Conference on Emerging Trends in Engineering & Technology. Nagpur, India: IEEE, 2009: 909-913.
[9]	CHEN Y, CHENG Y N, LIU H L. Application of improved wavelet adaptive threshold de-noising algorithm in FBG demodulation [J]. Optik, 2017, 132(1):243-248.
[10]	EL-SHEIMY N, HOU H Y, NIU X J. Analysis and modeling of inertial sensors using Allan variance [J].IEEE Transactions on Instrumentation and Measurement,2007, 57(1): 140-149.
[11]	NOURELDIN A, ARMSTRONG J, EL-SHAFIE A,et al. Accuracy enhancement of inertial sensors utilizing high resolution spectral analysis [J]. Sensors, 2012,12(9): 11638-11660.
[12]	QIN D H, YANG J H, ZHANG B. A novel dualthreshold denoising method by the wavelet transform[C]//International Conference on Intelligent Computing and Integrated Systems. Guilin, China: IEEE,2010: 171-174.
[13]	ZHU H J. Wavelet-based hybrid thresholding method for ultrasonic liver image denoising [J]. Journal of Shanghai Jiao Tong University (Science), 2015, 20(2):135-142.
[14]	ZHANG X P, DESAI M D. Adaptive denoising based on SURE risk [J]. IEEE Signal Processing Letters,1998, 5(10): 265-267.
[15]	HAIGHT F A. Handbook of the Poisson distribution[M]. New York, USA: John Wiley & Sons, 1967: 2-100.

Adaptive Dual Wavelet Threshold Denoising Function Combined with Allan Variance for Tuning FOG-SINS Filter

Adaptive Dual Wavelet Threshold Denoising Function Combined with Allan Variance for Tuning FOG-SINS Filter

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献 15

相关文章 1

编辑推荐

Metrics

本文评价