Research article

Design and evaluation of INS/GNSS loose and tight coupling applied to launch vehicle integrated navigation

  • Received: 30 August 2023 Revised: 13 December 2023 Accepted: 20 December 2023 Published: 25 December 2023
  • A satellite launcher requires accurate and reliable navigation to reach orbit safely. This challenging application includes vertical launch, flying through diverse layers of the atmosphere, discontinuous acceleration and stages separation, high terminal velocities and hostile temperature and vibration ranges. The Inertial Navigation System (INS) is typically coupled with a Global Navigation Satellite System (GNSS) receiver to achieve the desired solution quality along the complete ascent trajectory. Here, several INS/GNSS integrated navigation strategies are described and evaluated through numerical simulations. In particular, we evaluate different INS/GNSS integration using: loose coupling, tight coupling and an intermediate Kalman filter on the receiver to enhance the loosely coupled navigation solution. The main contributions are: a compensation model of tropospheric delays on the GNSS observables, a compensation of processing/communication delays on GNSS receiver outputs, covariance adaptations to consider vehicle's high dynamics, and an specific way to integrate the filtered solution and its covariance provided by the GNSS receiver. The tightly coupled integrated navigation proved to be the best choice to handle possible GNSS receiver positioning solution outages, to exploit more degrees of freedom for the compensation of GNSS observables and the possibility to make a cross validation of individual GNSS observables with INS-based information. This is specially important during the periods with a low number of satellites in view when the GNSS positioning solution can not be validated or even computed by the GNSS receiver alone. Finally, a test with hardware in the loop is provided to validate the numerical result for the selected tightly coupled INS/GNSS.

    Citation: Victor Cánepa, Pablo Servidia. Design and evaluation of INS/GNSS loose and tight coupling applied to launch vehicle integrated navigation[J]. Metascience in Aerospace, 2024, 1(1): 66-109. doi: 10.3934/mina.2024004

    Related Papers:

  • A satellite launcher requires accurate and reliable navigation to reach orbit safely. This challenging application includes vertical launch, flying through diverse layers of the atmosphere, discontinuous acceleration and stages separation, high terminal velocities and hostile temperature and vibration ranges. The Inertial Navigation System (INS) is typically coupled with a Global Navigation Satellite System (GNSS) receiver to achieve the desired solution quality along the complete ascent trajectory. Here, several INS/GNSS integrated navigation strategies are described and evaluated through numerical simulations. In particular, we evaluate different INS/GNSS integration using: loose coupling, tight coupling and an intermediate Kalman filter on the receiver to enhance the loosely coupled navigation solution. The main contributions are: a compensation model of tropospheric delays on the GNSS observables, a compensation of processing/communication delays on GNSS receiver outputs, covariance adaptations to consider vehicle's high dynamics, and an specific way to integrate the filtered solution and its covariance provided by the GNSS receiver. The tightly coupled integrated navigation proved to be the best choice to handle possible GNSS receiver positioning solution outages, to exploit more degrees of freedom for the compensation of GNSS observables and the possibility to make a cross validation of individual GNSS observables with INS-based information. This is specially important during the periods with a low number of satellites in view when the GNSS positioning solution can not be validated or even computed by the GNSS receiver alone. Finally, a test with hardware in the loop is provided to validate the numerical result for the selected tightly coupled INS/GNSS.



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    [1] Savage PG (1998) Strapdown Inertial Navigation Integration Algorithm Design Part 1: Attitude Algorithms. J Guid Control Dynam 21: 19–28. https://doi.org/10.2514/2.4228 doi: 10.2514/2.4228
    [2] Savage P (1998) Strapdown Inertial Navigation Integration Algorithm Design Part 2: Velocity and Position Algorithms. J Guid Control Dynam 21: 208–221. https://doi.org/10.2514/2.4242 doi: 10.2514/2.4242
    [3] Moreno A, Cánepa V, Giribet J, et al. (2021) Simulador GNSS para la Evaluación de Algoritmos de Navegación Integrada en Vehículos Aeroespaciales. XI Congreso Argentino de Tecnología Espacial.
    [4] Tewari A (2011) Advanced Control of Aircraft, Spacecraft and Rockets, Wiley.
    [5] Suresh BN, Sivan K (2015) Integrated Design for Space Transportation Systems, Springer.
    [6] Range Command Council, US Army White Sands Missile Range (2011) 324 Global Positioning and Inertial Measurements Range Safety Tracking Systems Commonality Standard.
    [7] Hu G, Gao B, Zhong Y, et al. (2019) Robust Unscented Kalman Filtering With Measurement Error Detection for Tightly Coupled INS/GNSS Integration in Hypersonic Vehicle Navigation. IEEE Access 7: 151409–151421. https://doi.org/10.1109/ACCESS.2019.2948317 doi: 10.1109/ACCESS.2019.2948317
    [8] Hu G, Wang W, Zhong Y, et al. (2018) A New Direct Filtering Approach to INS/GNSS Integration. Aerosp Sci Technol 77: 755–764. https://doi.org/10.1016/j.ast.2018.03.040 doi: 10.1016/j.ast.2018.03.040
    [9] Meng Y, Gao S, Zhong Y, et al. (2016) Covariance Matching Based Adaptive Unscented Kalman Filter for Direct Filtering in INS/GNSS Integration. Acta Astronaut 120: 171–181. https://doi.org/10.1016/j.actaastro.2015.12.014 doi: 10.1016/j.actaastro.2015.12.014
    [10] Hu G, Gao S, Zhong Y (2015) A Derivative UKF for Tightly Coupled INS/GPS Integrated Navigation. ISA T 56: 135–144. https://doi.org/10.1016/j.isatra.2014.10.006 doi: 10.1016/j.isatra.2014.10.006
    [11] Bernadi P (2013) Sistema de Navegación INS/GPS para un Cohete Suborbital Controlado, Engineering Thesis, Universidad de Buenos Aires.
    [12] Hu G, Gao B, Zhong Y, et al. (2020) Unscented Kalman Filter with Process Noise Covariance Estimation for Vehicular INS/GPS Integration System. Inform Fusion 64: 194–204. https://doi.org/10.1016/j.inffus.2020.08.005 doi: 10.1016/j.inffus.2020.08.005
    [13] Gao Z, Gu C, Yang J, et al. (2020) Random Weighting-Based Nonlinear Gaussian Filtering. IEEE Access 8: 19590–19605. https://doi.org/10.1109/ACCESS.2020.2968363 doi: 10.1109/ACCESS.2020.2968363
    [14] Gao B, Gao S, Hu G, et al. (2018) Maximum Likelihood Principle and Moving Horizon Estimation Based Adaptive Unscented Kalman Filter. Aerosp Sci Technol 73: 184–196. https://doi.org/10.1016/j.ast.2017.12.007 doi: 10.1016/j.ast.2017.12.007
    [15] Gao S, Hu G, Zhong Y (2015) Windowing and Random Weighting Based Adaptive Unscented Kalman Filter. Int J Adapt Control 29: 201–223. https://doi.org/10.1002/acs.2467 doi: 10.1002/acs.2467
    [16] Cánepa V, Servidia P, Giribet J (2022) Adaptive Extended Kalman Filter for Integrated Navigation in a Satellite Launch Vehicle. VI Congreso Bienal de la Sección Argentina del IEEE (ARGENCON), San Juan, Argentina. 1–8. https://doi.org/10.1109/ARGENCON55245.2022.9939949 doi: 10.1109/ARGENCON55245.2022.9939949
    [17] Bancroft S (1985) An Algebraic Solution of the GPS Equations, in IEEE Transactions on Aerospace and Electronic Systems. 1: 56–59. doi: 10.1109/TAES.1985.310538.
    [18] Cánepa V (2022) Navegación Integrada Fuertemente Acoplada para Vehículos Lanzadores de Satélites, Engineering Thesis, Universidad de Buenos Aires.
    [19] Gao B, Hu G, Gao S, et al. (2018) Multi-sensor Optimal Data Fusion for INS/GNSS/CNS Integration Based on Unscented Kalman Filter. Int J Control Autom Syst 16: 129–140. https://doi.org/10.1007/s12555-016-0801-4. doi: 10.1007/s12555-016-0801-4
    [20] Hu G, Gao S, Zhong Y, Gao B, Subic A, (2016) Matrix Weighted Multisensor Data Fusion for INS/GNSS/CNS Integration, Proceedings of the Institution of Mechanical Engineers, Part G. J Aerospace Eng 230: 1011–1026. https://doi.org/10.1177/0954410015602723 doi: 10.1177/0954410015602723
    [21] Hu G, Gao S, Zhong Y, et al. (2016) Modified Federated Kalman Filter for INS/GNSS/CNS Integration, Proceedings of the Institution of Mechanical Engineers, Part G. J Aerospace Eng 230: 30–44. https://doi.org/10.1177/0954410015586860. doi: 10.1177/0954410015586860
    [22] Parkinson B, Spilker J (1996) Global Positioning System: Theory and Applications. AIAA, Washington DC.
    [23] Kaniewski P, Gil R, Konatowski S (2016) Algorithms of Position and Velocity Estimation in GPS Receivers. Annu Navigation 23. https://doi.org/10.1515/aon-2016-0004 doi: 10.1515/aon-2016-0004
    [24] Sarunic P (2016) Development of GPS Receiver Kalman Filter Algorithms for Stationary, Low-Dynamics, and High-Dynamics Applications. Defense, Science and Technology Group, Australia, Technical Report DST-Group-TR-3260.
    [25] Gómez D, Langston C, Smalley R (2015) A Closed-form Solution for Earthquake Location in a Homogeneous Half-space Based on the Bancroft GPS Location Algorithm.
    [26] Cánepa V, Servidia P, Giribet J (2023) Aspectos Prácticos en la Navegación Integrada de Vehículos Lanzadores. XII Congreso Argentino de Tecnología Espacial.
    [27] España M (2019) Sistemas de Navegación Integrada con Aplicaciones, 2nd ed. Comisión Nacional de Actividades Espaciales. Available from: www.argentina.gob.ar/ciencia/conae/publicaciones.
    [28] Jazwinski A (1970) Stochastic Processes and Filtering Theory. Math Sci Eng 64.
    [29] Reif K, Günther S, Yaz E (1999) Stochastic Stability of the Discrete-time Extended Kalman Filter. IEEE 44.
    [30] Ford J, Coulter A (2001) Filtering for Precision Guidance: The Extended Kalman Filter. Aeronautical and Maritime Research Laboratory.
    [31] Rodríguez S, García J, Scillone G, et al. (2020) Dual-Antenna Dual-Band High Performance Cubesat-Compatible GPS Receiver. IEEE Lat Am T 18: 265–272. https://doi.org/10.1109/TLA.2020.9085279 doi: 10.1109/TLA.2020.9085279
    [32] López La Valle R, Rodriguez S, Garcia J (2018) Documento de Control de Interfaces (ICD) M3GR-EMB. SENYT-UNLP.
    [33] Klobuchar J (1987) Ionospheric Time-Delay Algorithms for Single-Frequency GPS Users. IEEE T Aero Elec Syst 3: 325–331.
    [34] Servidia P, Sánchez Peña R (2002) Thruster Design for Position/Attitude Control of Spacecraft. IEEE T Aero Elec Sys 38: 1172–1180. https://doi.org/10.1109/TAES.2002.1145741 doi: 10.1109/TAES.2002.1145741
    [35] Servidia P (2010) Control Allocation for Gimbaled/Fixed Thrusters. Acta Astronaut 66: 587–594. https://doi.org/10.1016/j.actaastro.2009.07.023 doi: 10.1016/j.actaastro.2009.07.023
    [36] Servidia P, Sánchez Peña R, (2005) Spacecraft Thruster Control Allocation Problems. IEEE T Automat Contr 50: 245–249. https://doi.org/10.1109/TAC.2004.841923 doi: 10.1109/TAC.2004.841923
    [37] Servidia P, Sánchez Peña R (2005) Practical Stabilization in Attitude Thruster Control. IEEE T Aerosp Elec Sys 41: 584–598. https://doi.org/10.1109/TAES.2005.1468750 doi: 10.1109/TAES.2005.1468750
    [38] Beaudoin Y, Desbiens A, Gagnon E, et al. (2018) Observability of Satellite Launcher Navigation with INS, GPS, Attitude Sensors and Reference Trajectory. Acta Astronaut 142: 277–288. https://doi.org/10.1016/j.actaastro.2017.10.038 doi: 10.1016/j.actaastro.2017.10.038
    [39] Van Dierendonk A, McGraw J, Grover Brown R (1984) Relationship Between Allan Variances and Kalman Filter Parameters. NASA Goddard Space Flight Center, Proc. of the 16th Ann. Precise Time and Time Interval (PTTI) Appl. and Planning Meeting 273–293.
    [40] Kaplan E, Hegarty C (2006) Understanding GPS: Principles and Applications, Artech House.
    [41] Grover Brown R, Hwang P (2012) Introduction to Random Signals and Applied Kalman Filtering. 4th ed. John Wiley & Sons, Inc.
    [42] KHV 1750 Inertial Measurement Unit Technical Manual (2013) KVH Industries.
    [43] Gomez E, Servidia P, España M (2019) Fast and Reliable Computation of Mean Orbit Elements for Autonomous Orbit Control, IAA-LA2-03-03, Proceedings of the 2nd IAA Latin American Symposium on Small Satellites, 69–76.
    [44] España M, Carrizo J, Giribet J (2019) Sensability and Excitability Metrics Applied to Navigation Systems Assessment. J Navigation 72: 1481–1495. https://doi.org/10.1017/S0373463319000328 doi: 10.1017/S0373463319000328
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