INS/GPS KF integration performance improvement based on accurate inertial sensors sochastic error modelling

• Autorzy: Shahrawy Ahmed , Radi Ahmed , Zahran Shady , Anis Wagdy

Identyfikatory

DOI

10.15199/48.2024.06.46

Warianty tytułu

PL

Poprawa wydajności integracji INS/GPS KF w oparciu o dokładne modelowanie błędów stochastycznych z czujników inercyjnych

• Języki publikacji: EN

Abstrakty

EN

Inertial Navigation Systems (INS) provides precise data for short time-period, but their accuracy diminishes over time, especially with low cost sensors. To maintain acceptable accuracy, sensor error components must be accurately calibrated/modelled. Different methods have been used to characterize the inertial sensors stochastic errors, including the Autocorrelation function (ACF), Allan variance (AV) and the Generalized Method of Wavelet Moments (GMWM). This paper focuses on the analysis of Micro-Electromechanical Systems (MEMS)-based inertial sensor errors under various conditions. The inertial sensor stochastic error processes are estimated using both the AV and the GMWM techniques. Based on the comparison between both stochastic analysis tools, the GMWM was selected and a GMWM-based model selection criteria is utilized to rank candidate error models. An extended 39-states integrated GNSS/INS navigation algorithm (based on the chosen error model) is proposed and compared with a standard 15- states integrated GNSS/INS navigation algorithm (based on 1st Gauss-Markov process for modelling the stochastic errors). The study analyses various stochastic error models using real data of Inertial Navigation System (INS) and Global Positioning System (GPS) with intended GPS signal outage periods. Results reveal enhanced position accuracy with the proposed algorithm and superior performance with GMWM-based error model over standard ACF-based one.

PL

Inercyjne systemy nawigacji (INS) dostarczają dokładnych danych przez krótki okres czasu, ale ich dokładność maleje z czasem, szczególnie w przypadku tanich czujników. Aby zachować akceptowalną dokładność, składowe błędu czujnika muszą być dokładnie skalibrowane/modelowane. Do charakteryzowania błędów stochastycznych czujników inercyjnych zastosowano różne metody, w tym funkcję autokorelacji (ACF), wariancję Allana (AV) i uogólnioną metodę momentów falkowych (GMWM). W artykule skupiono się na analizie błędów czujników inercyjnych opartych na systemach mikroelektromechanicznych (MEMS) w różnych warunkach. Procesy błędów stochastycznych czujnika inercyjnego są szacowane przy użyciu technik AV i GMWM. Na podstawie porównania obu narzędzi analizy stochastycznej wybrano GMWM, a kryteria wyboru modelu oparte na GMWM zastosowano do uszeregowania modeli potencjalnych błędów. Zaproponowano rozszerzony, 39-stanowy zintegrowany algorytm nawigacji GNSS/INS (oparty na wybranym modelu błędów) i porównano go ze standardowym 15-stanowym zintegrowanym algorytmem nawigacji GNSS/INS (opartym na pierwszym procesie Gaussa-Markowa do modelowania błędów stochastycznych). W pracy przeanalizowano różne modele błędów stochastycznych wykorzystując rzeczywiste dane z systemu nawigacji inercyjnej (INS) i globalnego systemu pozycjonowania (GPS) z przewidywanymi okresami zaniku sygnału GPS. Wyniki ujawniają zwiększoną dokładność pozycjonowania dzięki proponowanemu algorytmowi i lepszą wydajność dzięki modelowi błędów opartemu na GMWM w porównaniu ze standardowym modelem opartym na ACF.

Słowa kluczowe

EN

Inertial Navigation System; INS; Global Positioning System; GPS; autocorrelation function; ACF; Allan variance; generalized method of wavelet moments; GMWM; inertial sensors error; confidence interval; wavelet information criterion

PL

inercyjny system nawigacji; globalny system pozycjonowania; funkcja autokorelacji; INS; GPS; ACF

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2024
• Tom: R. 100, nr 6
• Strony: 219--226
• Opis fizyczny: Bibliogr. 36 poz., rys., tab.

Twórcy

autor

Shahrawy Ahmed

• University of Ain Shams

autor

Radi Ahmed

• Technical Research Center

autor

Zahran Shady

• University of Calgary

autor

Anis Wagdy

• University of Ain Shams

Bibliografia

• 1 A. Noureldin, T. B. Karamat, and J. Georgy, Fundamentals of inertial navigation, satellite-based positioning and their integration. Springer Berlin Heidelberg, 2013. doi: 10.1007/978- 3-642-30466-8.
• 2 G. Macgougan, G. Lachapelle, R. Nayak, and A. Wang, “OVERVIEW OF GNSS SIGNAL DEGRADATION PHENOMENA.”
• 3 O. J. Woodman, “Number 696 An introduction to inertial navigation An introduction to inertial navigation,” 2007, [Online]. Available. http://www.cl.cam.ac.uk/http://www.cl.cam.ac.uk/techreports/
• 4 M. S. Grewal, L. R. (Lawrence R. Weill, and A. P. Andrews, Global positioning systems, inertial navigation, and integration. John Wiley, 2001.
• 5 W. Abdel-Hamid, “UCGE Reports Accuracy Enhancement of Integrated MEMS-IMU/GPS Systems for Land Vehicular Navigation Applications,” 2020. [Online]. Available: http://www.geomatics.ucalgary.ca/links/GradTheses.html
• 6 X. Niu, S. Nasser, C. Gooddall, and N. El-Sheimy, “A universal approach for processing any MEMS inertial sensor configuration for land-vehicle navigation,” Journal of Navigation, vol. 60, no. 2, pp. 233–245, May 2007, doi: 10.1017/S0373463307004213.
• 7 D. Ünsal, “ESTIMATION OF DETERMINISTIC AND STOCHASTIC IMU ERROR PARAMETERS A THESIS SUBMITTED TO THE GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES OF MIDDLE EAST TECHNICAL UNIVERSITY,” 2012
• 6 W. S. Flenniken, J. H. Wall, and D. M. Bevly, “Characterization of Various IMU Error Sources and the Effect on Navigation Performance.”
• 9 Z. Ashraf and W. Nafees, “Error modeling and analysis of inertial measurement unit using stochastic and deterministic techniques,” in Advanced Materials Research, 2012, pp. 4447– 4455. doi: 10.4028/www.scientific.net/AMR.403-408.4447.
• 10 UCGE Reports Improving the Inertial Navigation System (INS) Error Model for INS and INS/DGPS Applications,” 2018.. Available: http://www.geomatics.ucalgary.ca/links/GradTheses.html
• 11 M. Park and Y. Gao, “Error and Performance Analysis of MEMS-based Inertial Sensors with a Low-cost GPS Receiver,” Sensors, vol. 8, pp. 2240–2261, 2008, [Online]. Available: www.mdpi.org/sensors
• 12 R. Z. Z. G. Z. C. J. D. Xiaoji Niu, “Micro-machined attitude measurement unit with application in satellite TV antenna stabilization,” Jan. 2002.
• 13 J. C. M. Zhihua Zhang, “Autoregressive Moving Average Models,” in Mathematical and Physical Fundamentals of Climate Change, Second., Beijing: elsevier, 2015, pp. 239–289.
• 14 N. El-Sheimy, H. Hou, and X. Niu, “Analysis and modeling of inertial sensors using allan variance,” IEEE Trans Instrum Meas, vol. 57, no. 1, 2008, doi: 10.1109/TIM.2007.908635.
• 15 J. R. Evans et al., “Method for calculating self-noise spectra and operating ranges for seismographic inertial sensors and recorders,” Seismological Research Letters, vol. 81, no. 4, 2010, doi: 10.1785/gssrl.81.4.640.
• 16 A. G. Quinchia, G. Falco, E. Falletti, F. Dovis, and C. Ferrer, “A comparison between different error modeling of MEMS applied to GPS/INS integrated systems,” Sensors (Switzerland), vol. 13, no. 8, pp. 9549–9588, 2013, doi: 10.3390/s130809549.
• 17 J. Balamuta, S. Guerrier, R. Molinari, and W. Yang, “A Computationally Efficient Framework for Automatic Inertial Sensor Calibration,” Mar. 2016, [Online]. Available: http://arxiv.org/abs/1603.05297
• 18 S. Guerrier, J. Skaloud, Y. Stebler, and M. P. Victoria-Feser, “Wavelet-variance-based estimation for composite stochastic processes,” J Am Stat Assoc, vol. 108, no. 503, 2013, doi: 10.1080/01621459.2013.799920.
• 19 J. Balamuta, R. Molinari, S. Guerrier, and W. Yang, “The gmwm R package: a comprehensive tool for time series analysis from state-space models to robustness,” Jul. 2016, [Online]. Available: http://arxiv.org/abs/1607.04543
• 20 M. El-Diasty, A. El-Rabbany, and S. Pagiatakis, “Temperature variation effects on stochastic characteristics for low-cost MEMS-based inertial sensor error,” Meas Sci Technol, vol. 18, no. 11, pp. 3321–3328, Nov. 2007, doi: 10.1088/0957- 0233/18/11/009.
• 21 Y. Stebler, S. Guerrier, J. Skaloud, R. Molinari, and M.-P. Victoria-Feser, “Study of MEMS-based Inertial Sensors Operating in Dynamic Conditions,” 2014/
• 22 UCGE Reports Improving the Inertial Navigation System (INS) Error Model for INS and INS/DGPS Applications,” 2018.. Available: http://www.geomatics.ucalgary.ca/links/GradTheses.html
• 23 M. S. Keshner, “l/f Noise,” 1982.
• 24 W. Abdel-Hamid, “Accuracy enhancement of integrated MEMS IMU/GPS systems for land vehicular navigation applications,” University of Calgary, Calgary, 2005.
• 25 A. Radi, “Non-linear Error Modeling for MEMS-based IMUs,” 2018.
• 26 ED610086”.
• 27 vdoc.pub_wavelets-in-chemistry/
• 28 FTP SITE NOW AVAILABLE
• 29 S. Nassar, N. El-Sheimy, K.-P. Schwarz, and A. Noureldin, “Modeling Inertial Sensor Errors Using Autoregressive (AR) Models,” 2003. [Online]. Available: https://www.researchgate.net/publication/281628063
• 30 X. Niu et al., “Using Allan variance to analyze the error characteristics of GNSS positioning,” GPS Solutions, vol. 18, 2, pp. 231–242, Apr. 2014, doi: 10.1007/s10291-013-0324-x.
• 31 H. Hou, “Modeling Inertial Sensors Errors Using Allan Variance UCGE Reports,” 2004. [Online]. Available: http://www.geomatics.ucalgary.ca/links/GradTheses.html
• 32 A. Radi, S. Nassar, and N. El-Sheimy, “Stochastic Error Modeling of Smartphone Inertial Sensors for Navigation in Varying Dynamic Conditions,” Gyroscopy and Navigation, vol. 9, no. 1, pp. 76–95, Jan. 2018, doi: 10.1134/S2075108718010078.
• 33 J. Balamuta, R. Molinari, S. Guerrier, and W. Yang, “The gmwm R package: a comprehensive tool for time series analysis from state-space models to robustness,” Jul. 2016, [Online]. Available: http://arxiv.org/abs/1607.04543
• 34 G. Bakalli et al., “Multi-Signal Approaches for Repeated Sampling Schemes in Inertial Sensor Calibration,” IEEE Transactions on Signal Processing, vol. 71, pp. 1103–1114, 2023, doi: 10.1109/TSP.2023.3262179.
• 35 A. Radi, G. Bakalli, S. Guerrier, N. El-Sheimy, A. B. Sesay, and R. Molinari, “A Multisignal Wavelet Variance-Based Framework for Inertial Sensor Stochastic Error Modeling,” IEEE Trans Instrum Meas, vol. 68, no. 12, pp. 4924–4936, Dec. 2019, doi: 10.1109/TIM.2019.2899535
• 36 C. Military Technical College and Institute of Electrical and Electronics Engineers, 2020 12th International Conference on Electrical Engineering (ICEENG).

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).