Certificate of Registration Media number Эл #ФС77-53688 of 17 April 2013. ISSN 2308-6033. DOI 10.18698/2308-6033
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Features of using Kalman filter for algorithmic compensation of inertial navigation system errors

Published: 06.12.2018

Authors: Egorushkin A.Yu., Salychev O.S.

Published in issue: #12(84)/2018

DOI: 10.18698/2308-6033-2018-12-1834

Category: Aviation and Rocket-Space Engineering | Chapter: Aircraft Dynamics, Ballistics, Motion Control

Algorithmic error compensation is the main method for improving the accuracy of inertial navigation systems (INS). Its implementation requires the synthesis of estimation  algorithms, which includes the formation of a model of the estimated dynamic system state vector. The type and order of the model determine the accuracy and quality of the estimates obtained as a result of filtering. The study proposes the concurrent use of several different models, for each of them its own estimation algorithm is synthesized, which is the most adequate to the transport motion mode. The real data for the system installed on the helicopter show the advantages of such computational scheme. We also suggest introducing the adaptive tuning into the filtering algorithm, which will increase the accuracy of the estimates and improve the quality of transients of estimates convergence.  A specific example shows the positive effect of such adaptive tuning introduced into the algorithm

[1] Salychev O.S. MEMS-based Inertial Navigation: Expectations and Reality. Moscow, BMSTU Publ., 2012, 208 p.
[2] Salychev O.S. Applied Inertial Navigation: Problems and Solutions. Moscow, BMSTU Press, 2004, 304 p.
[3] Kurdyukov A.P., Stepanov O.A. Avtomatica i telematika — Automation and Remote Control, 2016, no. 1, pp. 3–4.
[4] Stepanov O.A. Optimal and sub-optimal filtering in integrated navigation systems. Aerospace Navigation Systems. NY, Yohn & Sons, 2016, pp. 244–298. DOI: 10.1002/9781119163060.ch8
[5] Wei Li, Jinling Wang. Effective Adaptive Kalman Filter for MEMS IMU/Magnetometers Intergrated Attitude and Heading Reference System. The Journal of Navigation, 2013, vol. 66, iss. 1, pp. 99–113.
[6] Broxmeyer Ch. Inertial navigation systems. Boston, McGraw-Hill Book Company, 254 p. [In Russ.: Broxmeyer Ch. Sistemy inertsialnoy navigatsii. Leningrad, Sudostroenie Publ., 1967, 278 p.].
[7] Titterton D.H., Weston J.L. Strapdown Inertial Navigation Technology. 2nd ed. Stevenage, The Institution of Electrical Engineers, 2004, 581 p.
[8] Pitman G.R. Inertial Guidance (Space Technology S.). John Wiley & Sons Inc., 1962, 481 p. [In Russ.: Pitman G.R. Inertsialnye systemy upravleniya. Moscow, Voennoye izdatelstvo ministerstva oborony USSR, 1964, 453 p.].
[9] Matveev V.V. Inertsialnye navigatsionye systemy [Inertial navigation systems]. Tula, TulSU, Publ., 2012, 199 p.
[10] Groves P. D. Principles of GNSS, Inertial and Multisensor Integrated Navigation Systems. Norwood, Artech House, 2013, 800 p.
[11] Motwani A., Sharma S.K., Sutton R., Culverhouse P. Interval Kalman Filtering in Navigation System Design for an Uninhabited Surface Vehicle. The Journal of Navigation, 2013, vol. 66, iss. 5, pp. 639–652.
[12] Matveev V.V., Raspopov V.Ya. Osnovy postroeniya besplatformennykh inertsialnykh navigatsionnykh system [Fundamentals of Strapdown Inertial Navigation System Design]. St. Petersburg, Concern CRSI Elektropribor, JSC Publ., 2009, 280 p.
[13] Korkishko Yu.N., et al. Giroskopiya i navigatsiya — Gyroscopy and Navigation, 2014, no. 1(84), pp. 14–25.