An innovation based random weighting estimation mechanism for denoising fiber optic gyro drift signal

Abstract In Interferometric Fiber Optic Gyroscope (IFOG), the diminution of random noise and drift error is a critical task. These errors degrade the performance of IFOG. In this paper, a modified adaptive Kalman gain correction (AKFG) algorithm is proposed to denoise IFOG signal. The covariance matrix of innovation sequence is estimated using weighted average window method in which the weights are randomly generated in the range [0, 1]. Innovation based random weighted estimation (IRWE)-AKFG is applied to denoise the IFOG drift signal. The Kalman gain is adaptively updated using the covariance matrix of innovation sequence. The proposed algorithm is applied for denoising IFOG signal under static and dynamic environment. Allan variance method is used to analyze and quantify the stochastic errors in IFOG sensor. The performance of the proposed algorithm is compared with Conventional Kalman filter (CKF) and the simulation results reveal that the proposed algorithm is an efficient algorithm for denoising the IFOG signal.

[1]  Zhihua Feng,et al.  Random Weighting Estimation of White Noise Error Characteristic in Integrated INS/GPS/SAR System , 2006, 2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA'06).

[2]  Samrat L. Sabat,et al.  Efficient hybrid Kalman filter for denoising fiber optic gyroscope signal , 2013 .

[3]  Jiang Hong,et al.  State space modeling of random drift rate in high-precision gyro , 1996 .

[4]  Jing Liu,et al.  An Improved Adaptive Filtering Algorithm with Applications in Integrated Navigation , 2012, 2012 Third International Conference on Digital Manufacturing & Automation.

[5]  Masayoshi Tomizuka,et al.  Multiple model adaptive estimation of satellite attitude using MEMS gyros , 2011, Proceedings of the 2011 American Control Conference.

[6]  Cezary Kownacki,et al.  Optimization approach to adapt Kalman filters for the real-time application of accelerometer and gyroscope signals' filtering , 2011, Digit. Signal Process..

[7]  Caterina Ciminelli,et al.  Advances in Gyroscope Technologies , 2011 .

[8]  Kanshi Yamamoto,et al.  Development of high grade fiber-optic gyroscopes , 1997 .

[9]  Chang-Hua Hu,et al.  An effective hybrid approach based on grey and ARMA for forecasting gyro drift , 2008 .

[10]  Samrat L. Sabat,et al.  A modified Sage-Husa adaptive Kalman filter for denoising Fiber Optic Gyroscope signal , 2012, 2012 Annual IEEE India Conference (INDICON).

[11]  Weifeng Tian,et al.  Temperature drift modelling of fibre optic gyroscopes based on a grey radial basis function neural network , 2004 .

[12]  Kezhi Zhang,et al.  A novel adaptive filter mechanism for improving the measurement accuracy of the fiber optic gyroscope in the maneuvering case , 2007 .

[13]  Naser El-Sheimy,et al.  Wavelet Analysis For Improving INS and INS/DGPS Navigation Accuracy , 2005, Journal of Navigation.

[14]  R. Priyadarshini,et al.  非較正分光計,電流測定,数値シミュレーションを適用した空気中誘電体バリア放電の定量的特性化 , 2012 .

[15]  A. H. Mohamed,et al.  Adaptive Kalman Filtering for INS/GPS , 1999 .

[16]  Bijan Shirinzadeh,et al.  Random weighting estimation for fusion of multi-dimensional position data , 2010, Inf. Sci..

[17]  Yongmin Zhong,et al.  Random weighting estimation method for dynamic navigation positioning , 2011 .

[18]  Renaud Kiefer,et al.  An optimal open-loop multimode fiber gyroscope for rate-grade performance applications , 2011 .

[19]  Chris Hide,et al.  Adaptive Kalman Filtering for Low-cost INS/GPS , 2002, Journal of Navigation.

[20]  Mohinder S. Grewal,et al.  Kalman Filtering: Theory and Practice Using MATLAB , 2001 .

[21]  Anthony Lawrence,et al.  Modern Inertial Technology: Navigation, Guidance, and Control , 1993 .

[22]  Afsar Saranli,et al.  Characterization and calibration of MEMS inertial sensors for state and parameter estimation applications , 2012 .

[23]  Wei Gao,et al.  Research on modeling and compensation method of fiber optic gyro' random error , 2005, IEEE International Conference Mechatronics and Automation, 2005.

[24]  Ningfang Song,et al.  Stellar sensor based nonlinear model error filter for gyroscope drift extraction , 2010 .

[25]  Bijan Shirinzadeh,et al.  Random weighting estimation of parameters in generalized Gaussian distribution , 2008, Inf. Sci..

[26]  Shesheng Gao,et al.  The Research of Data Fusion Method for Sample Mean Random Weighting Estimation , 2006, 2006 IEEE International Conference on Information Acquisition.

[27]  Jagannath Nayak,et al.  Fiber-optic gyroscopes: from design to production [Invited] , 2011 .

[28]  Xiaoji Niu,et al.  Analysis and Modeling of Inertial Sensors Using Allan Variance , 2008, IEEE Transactions on Instrumentation and Measurement.

[29]  Bian Hong-wei,et al.  IAE-adaptive Kalman filter for INS/GPS integrated navigation system 1 1 This project was supported by the National Natural Science Foundation of China (40125013 & 40376011). , 2006 .

[30]  Zhihua Jin,et al.  Study on GPS attitude determination system aided INS using adaptive Kalman filter , 2005 .

[31]  C. Rizos,et al.  Improving Adaptive Kalman Estimation in GPS/INS Integration , 2007, Journal of Navigation.

[32]  Weifeng Tian,et al.  EMD- and LWT-based stochastic noise eliminating method for fiber optic gyro , 2011 .

[33]  R. Mehra.  On-line identification of linear dynamic systems with applications to Kalman filtering , 1971 .

[34]  Anthony Lawrence,et al.  Modern Inertial Technology , 1993 .

[35]  Chong Shen,et al.  Study on temperature error processing technique for fiber optic gyroscope , 2013 .

[36]  Fang Jiancheng,et al.  Dynamic angular velocity modeling and error compensation of one-fiber fiber optic gyroscope (OFFOG) in the whole temperature range , 2012 .

[37]  A. A. Odintsov,et al.  Calibration of fiber-optic gyros within strapdown inertial measurement units , 2012 .

[38]  Li Duan,et al.  A Kalman filter approach based on random drift data of Fiber Optic Gyro , 2011, 2011 6th IEEE Conference on Industrial Electronics and Applications.