Skip to main content Accessibility help
×
Home
Hostname: page-component-59b7f5684b-ns2hh Total loading time: 0.441 Render date: 2022-10-04T11:11:09.993Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "displayNetworkTab": true, "displayNetworkMapGraph": false, "useSa": true } hasContentIssue true

Adaptive Kalman Filtering with Recursive Noise Estimator for Integrated SINS/DVL Systems

Published online by Cambridge University Press:  15 August 2014

Wei Gao
Affiliation:
(College of Automation, Harbin Engineering University, Harbin, China)
Jingchun Li*
Affiliation:
(College of Automation, Harbin Engineering University, Harbin, China)
Guangtao Zhou
Affiliation:
(College of Automation, Harbin Engineering University, Harbin, China)
Qian Li
Affiliation:
(College of Automation, Harbin Engineering University, Harbin, China)
*

Abstract

This paper considers the estimation of the process state and noise parameters when the statistics of the process and measurement noise are unknown or time varying in the integration system. An adaptive Kalman Filter (AKF) with a recursive noise estimator that is based on maximum a posteriori estimation and one-step smoothing filtering is proposed, and the AKF can provide accurate noise statistical parameters for the Kalman filter in real-time. An exponentially weighted fading memory method is introduced to increase the weights of the recent innovations when the noise statistics are time varying. Also, the innovation covariances within a moving window are averaged to correct the noise statistics estimator. Experiments on the integrated Strapdown Inertial Navigation System (SINS)/ Doppler Velocity Log (DVL) system show that the proposed AKF improves the estimation accuracy effectively and the AKF is robust in the presence of vigorous-manoeuvres and rough sea conditions.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Al-Shabi, M., Gadsden, S.A. and Habibi, S.R. (2012). Kalman filtering strategies utilizing the chattering effects of the smooth variable structure filter. Signal Processing, 93, 420431.CrossRefGoogle Scholar
Bavdekar, V.A., Deshpande, A.P. and Patwardhan, S.C. (2011). Identification of process and measurement noise covariance for state and parameter estimation using extended Kalman filter. Journal of Process Control, 21(4), 585601.CrossRefGoogle Scholar
Ding, W., Wang, J., Rizos, C. and Kinlyside, D. (2007). Improving adaptive Kalman estimation in GPS/INS integration. Journal of Navigation, 60(3), 517529.CrossRefGoogle Scholar
Fang, J.C. and Yang, S. (2011). Study on innovation adaptive EKF for in-flight alignment of airborne POS. Instrumentation and Measurement, IEEE Transactions on, 60(4), 13781388.Google Scholar
Gao, X., You, D. and Katayama, S. (2012). Seam Tracking Monitoring Based on Adaptive Kalman Filter Embedded Elman Neural Network During High-Power Fiber Laser Welding. Industrial Electronics, IEEE Transactions on, 59(11), 43154325.CrossRefGoogle Scholar
Gustafsson, F. (2000). Adaptive filtering and change detection. John Wiley & Sons, New York.Google Scholar
Hewitson, S. and Wang, J. (2006). GPS/GLONASS/Galileo receiver autonomous integrity monitoring (RAIM) performance analysis. GPS Solutions, 10(3), 155170.CrossRefGoogle Scholar
Hewitson, S. and Wang, J. (2010). Extended Receiver Autonomous Integrity Monitoring (eRAIM) for GNSS/INS Integration. Journal of Surveying Engineering, 136(1), 1322.CrossRefGoogle Scholar
Jwo, D.J. and Weng, T.P. (2008). An adaptive sensor fusion method with applications in integrated navigation. Journal of Navigation, 61(4), 705721.CrossRefGoogle Scholar
Karasalo, M. and Hu, X. (2011). An optimization approach to adaptive Kalman filtering. Automatica, 47(8), 17851793.CrossRefGoogle Scholar
Knight, N.L., Wang, J. and Rizos, C. (2010). Generalised Measures of Reliability for Multiple Outliers. Journal of Geodesy, 84(10), 625635.CrossRefGoogle Scholar
Mehra, R. (1970). On the identification of variances and adaptive Kalman filtering. Automatic Control, IEEE Transactions on, 15(2), 175184.CrossRefGoogle Scholar
Mehra, R. (1972). Approaches to adaptive filtering. Automatic Control, IEEE Transactions on, 17(5), 693698.CrossRefGoogle Scholar
Mohamed, A.H. and Schwarz, K.P. (1999). Adaptive Kalman filtering for INS/GPS. Journal of Geodesy, 73(4), 193203.CrossRefGoogle Scholar
Myers, K. and Tapley, B. (1976). Adaptive sequential estimation with unknown noise statistics. Automatic Control, IEEE Transactions on, 21(4), 520523.CrossRefGoogle Scholar
Odelson, B.J., Rajamani, M.R. and Rawlings, J.B. (2006). A new autocovariance least-squares method for estimating noise covariances. Automatica, 42(2), 303308.CrossRefGoogle Scholar
Sage, A.P. and Husa, G.W. (1969a). Adaptive filtering with unknown prior statistics. In Proceedings of joint automatic control conference, Boulder, 760769.Google Scholar
Sage, A.P. and Husa, G. W. (1969b). Algorithms for sequential adaptive estimation of prior statistics. In Adaptive Processes (8th) Decision and Control, 1969 IEEE Symposium on, 6170.Google Scholar
Sangsuk-Iam, S. and Bullock, T.E. (1990). Analysis of discrete-time Kalman filtering under incorrect noise covariances. Automatic Control, IEEE Transactions on, 35(12), 13041309.CrossRefGoogle Scholar
Sarkka, S. and Nummenmaa, A. (2009). Recursive noise adaptive Kalman filtering by variational Bayesian approximations. Automatic Control, IEEE Transactions on, 54(3), 596600.CrossRefGoogle Scholar
Wang, J. (2000). Stochastic modeling for RTK GPS/GLONASS positioning. Navigation: Journal of the US Institute of Navigation, 46(4), 297305.CrossRefGoogle Scholar
Li, W., Wang, J., Lu, L. and Wu, W. (2013). A novel scheme for DVL-aided SINS in-motion alignment using UKF techniques. Sensors, 13, 10461063.CrossRefGoogle ScholarPubMed
Zhang, Y. (2013). An approach of DVL-aided SDINS alignment for in-motion vessel. Optik, 124, 62706275.CrossRefGoogle Scholar
Zhou, J., Knedlik, S. and Loffeld, O. (2010). INS/GPS Tightly-coupled integration using adaptive unscented particle filter. Journal of Navigation, 63(3), 491511.CrossRefGoogle Scholar
58
Cited by

Save article to Kindle

To save this article to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Adaptive Kalman Filtering with Recursive Noise Estimator for Integrated SINS/DVL Systems
Available formats
×

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox.

Adaptive Kalman Filtering with Recursive Noise Estimator for Integrated SINS/DVL Systems
Available formats
×

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive.

Adaptive Kalman Filtering with Recursive Noise Estimator for Integrated SINS/DVL Systems
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *