A Novel Sensor Integration Scheme for an Aided Inertial Navigation System Based on a Generalized PID Filter in the Presence of Observation Uncertainty

Document Type : Original Article


1 Department of Electrical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran

2 Department of Electrical Engineering, Kazerun Branch, Salman Farsi University, Kazerun, Iran


Implementing a proper integration scheme plays an important role in the performance of integrated navigation systems. Not only does employing a more reliable estimation method improve the accuracy of the integrated navigation system, but this can lead to a more robust solution in the presence of different types of uncertainties. Implementing an integration scheme that has a robust and simple structure is a challenging issue in the design of integrated navigation systems. By inspiring from the concept of PID control, this paper proposes a robust integration scheme for aided inertial navigation systems in the presence of aiding sensor measurement uncertainties. The proposed filter combines the concept of proportional-integral-derivative control theory and the standard Kalman filter estimator to improve the performance of the integration scheme. Thanks to the integral and derivative parts added to the proposed scheme, the integrated system attains a faster and more robust solution in the presence of observation errors and uncertainties. The simulation case studies validate the superior efficacy and capability of the proposed scheme compared to the integration method based on the standard Kalman filter.

Graphical Abstract

A Novel Sensor Integration Scheme for an Aided Inertial Navigation System Based on a Generalized PID Filter in the Presence of Observation Uncertainty


Main Subjects

  1. Noureldin A, Karamat TB, Georgy J. Fundamentals of inertial navigation, satellite-based positioning and their integration: Springer Science & Business Media; 2012.https://doi.org/10.1007/978-3-642-30466-8
  2. El-Sheimy N, Youssef A. Inertial sensors technologies for navigation applications: State of the art and future trends. Satellite Navigation. 2020;1(1):1-21. https://doi.org/10.1186/s43020-019-0001-5
  3. Farhangian F, Benzerrouk H, Landry Jr R. Opportunistic in-flight INS alignment using LEO satellites and a rotatory IMU platform. Aerospace. 2021;8(10):280.  https://doi.org/10.3390/aerospace8100280
  4. Ghaderi F, Toloei A, Ghasemi R. Quadrotor Control for Tracking Moving Target, and Dynamic Obstacle Avoidance Based on Potential Field Method. International Journal of Engineering, Transactions A: Basics. 2023;36(10):1720-32. https://doi.org/10.5829/IJE.2023.36.10A.01
  5. Wang Q, Liu K, Cao Z. System noise variance matrix adaptive Kalman filter method for AUV INS/DVL navigation system. Ocean Engineering. 2023;267:113269. https://doi.org/10.1016/j.oceaneng.2022.113269
  6. Siddharth D, Saini D, Singh P. An efficient approach for edge detection technique using kalman filter with artificial neural network. International Journal of Engineering, Transactions C: Aspects. 2021;34(12):2604-10. https://doi.org/10.5829/IJE.2021.34.12C.04
  7. Valizadeh A, Hamidi H. Improvement of navigation accuracy using tightly coupled kalman filter. International Journal of Engineering, Transactions B: Applications. 2017;30(2):215-23.  https://doi.org/10.5829/idosi.ije.2017.30.02b.08
  8. Hooshmand M, Yaghobi H, Jazaeri M. Irradiation and Temperature Estimation with a New Extended Kalman Particle Filter for Maximum Power Point Tracking in Photovoltaic Systems. International Journal of Engineering, Transactions C: Aspects. 2023;36(6):1099-113.  https://doi.org/10.5829/IJE.2023.36.06C.08
  9. Morales LA, Fabara P, Pozo DF. An Intelligent Controller Based on LAMDA for Speed Control of a Three-Phase Inductor Motor. Emerging Science Journal. 2023;7(3):676-90. https://doi.org/10.28991/ESJ-2023-07-03-01
  10. Bagherzadeh SZ, Toosizadeh S. Eye tracking algorithm based on multi model Kalman filter. HighTech and Innovation Journal. 2022;3(1):15-27. https://doi.org/10.28991/HIJ-2022-03-01-02
  11. Wirawan IMA, Wardoyo R, Lelono D, Kusrohmaniah S. Modified Weighted Mean Filter to Improve the Baseline Reduction Approach for Emotion Recognition. Emerging Science Journal. 2022;6(6):1255-73. http://dx.doi.org/10.28991/ESJ-2022-06-06-03
  12. Rahgoshay MA, Karimaghaie P. Robust inā€field estimation and calibration approach for strapdown inertial navigation systems accelerometers bias acting on the vertical channel. IET Radar, Sonar & Navigation. 2020;14(3):407-14. https://doi.org/10.1049/iet-rsn.2019.0359
  13. Rocha KD, Terra MH. Robust Kalman filter for systems subject to parametric uncertainties. Systems & Control Letters. 2021;157:105034. https://doi.org/10.1016/j.sysconle.2021.105034
  14. Wang D, Dong Y, Li Z, Li Q, Wu J. Constrained MEMS-based GNSS/INS tightly coupled system with robust Kalman filter for accurate land vehicular navigation. IEEE Transactions on Instrumentation and Measurement. 2019;69(7):5138-48. https://doi.org /10.1109/TIM.2019.2955798
  15. Zhu H, Zhang G, Li Y, Leung H. An adaptive Kalman filter with inaccurate noise covariances in the presence of outliers. IEEE Transactions on Automatic Control. 2021;67(1):374-81.  https://doi.org /10.1109/TAC.2021.3056343
  16. Farhangian F, Landry Jr R. Accuracy improvement of attitude determination systems using EKF-based error prediction filter and PI controller. Sensors. 2020;20(14):4055. https://doi.org/10.3390/s20144055
  17. Setoodeh P, Habibi S, Haykin S. Kalman Filter. 2022. https://doi.org /10.1002/9781119078166.ch5
  18. Zhang J, He X, Zhou D. Generalised proportional–integral–derivative filter. IET Control Theory & Applications. 2016;10(17):2339-47. https://doi.org/10.1049/iet-cta.2015.0610
  19. Rahgoshay MA, Karimaghaie P, Shabaninia F. Robust inertial frame-based alignment of fiber-optic gyro strapdown inertial navigation systems using a generalized proportional–integral–derivative filter. Optical Engineering. 2017;56(9):095102-. https://doi.org/ 10.1117/1.OE.56.9.095102.
  20. Rahgoshay MA, Karimaghaie P, Shabaninia F. Initial alignment of fiber-optic inertial navigation system with large misalignment angles based on generalized proportionalintegral-derivative filter. International Journal on Smart Sensing & Intelligent Systems. 2017;10(3). https://doi.org/10.21307/ijssis-2017-226
  21. Aggarwal P. MEMS-based integrated navigation: Artech House; 2010. https://doi.org/10.1186/s43020-019-0001-5
  22. Zhang W, Ghogho M, Yuan B. Mathematical model and matlab simulation of strapdown inertial navigation system. Modelling and Simulation in Engineering. 2012;2012.  https://doi.org/10.1155/2012/264537