Известия высших учебных заведений России: Радиоэлектроника (May 2023)

Random Noise Suppression Method for Inertial Sensors Based on Complexing an AR Model and Adaptive SRUKF Kalman Filter under the PINS Alignment on a Stationary Platform

  • Trong Yen Nguyen,
  • Quoc Khanh Nguyen,
  • Van Khoi Nguyen

DOI
https://doi.org/10.32603/1993-8985-2023-26-2-101-119
Journal volume & issue
Vol. 26, no. 2
pp. 101 – 119

Abstract

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Introduction. In the gyrocompassing mode, the initial heading angle of a platformless inertial navigation system (PINS) is determined based on the data obtained from accelerometers and gyroscopes that measure the projections of the gravitational acceleration vector and the Earth’s angular velocity vector on the axes of the body coordinates system in the PINS initial stationary mode. Due to unavoidable circumstances, such as bias instability and random noise in the accelerometer and gyroscope signals, much time is required to obtain the sufficient amount of sensor data for achieving the necessary accuracy of useful measurement values by the averaging method. In this context, in order to reduce the time of the gyrocompassing mode, data processing methods should be used to eliminate the bias instability and random noise in the signals received from PINS inertial sensors.Aim. To develop a method for suppressing random noise and reducing bias instability in the signals of inertial sensors, thereby reducing the time of the gyrocompassing mode of PINS and providing for the required accuracy of its initial heading angle determination.Materials and methods. An autoregressive (AR) model was used to simulate random noise in the measured sensor signals followed by its filtering using a Sage-window square-root unscented Kalman filter (SW-SRUKF).Results. A mathematical model describing random noise in the PINS inertial sensors in the stationary mode was derived. A methodology for suppressing random noise was proposed. The effectiveness of the proposed method was tested on actual data, with the results presented in the form of figures and tables.Conclusion. A method for eliminating the bias instability and random noise of PINS accelerometers and gyroscopes was proposed based on AR model and SW-SRUKF. The accuracy and effectiveness of the proposed method was confirmed by processing actual inertial sensor data. The results obtained are significant for reducing the initial alignment time of a PINS in the gyrocompassing mode.

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