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A memory-based filter for long-term error de-noising of MEMS-Gyros

Abbasi, J ; Sharif University of Technology | 2022

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  1. Type of Document: Article
  2. DOI: 10.1109/TIM.2022.3178964
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2022
  4. Abstract:
  5. The navigation algorithms which use inertial measurement units (IMUs), such as inertial navigation systems (INSs), always suffer from intrinsic accumulated errors. Bias in gyros induces a significant drift in navigation output especially when micro-electro-mechanical sensor (MEMS) type is used. This error has high-and low-frequency components. De-noising of the long-term error (LTE) (the low-frequency component) is more challenging due to undeterministic behavior and overlapping with carrier motion in the low-frequency band. In this article, a method for de-noising of long-term MEMS-based gyro is presented. In this approach, an auto-regressive (AR) model for the LTE is developed which is being used as the process part of a Kalman filter. To separate the low-band motion dynamic from LTE in the measurement part of the Kalman Filter, the last time epoch of gyro data is subtracted from the current time epoch (memory-based filter). Some static and dynamic experiments have been done for the algorithm evaluation. The static test shows the reduction of LTE by 50%. Also, the method is invoked in INS/Doppler velocity log (DVL) integrated navigation system as a gyro prefilter. The results show that drift and point-to-point final error in position are reduced between 4% and 70% by invoking the de-noising method for gyros. © 1963-2012 IEEE
  6. Keywords:
  7. Auto-regressive (AR) model ; Bias de-noising ; Long-term errors (LTEs) ; Memory-based filtering ; Micro-electro-mechanical sensor (MEMS) ; Air navigation ; Dynamics ; Errors ; Inertial navigation systems ; Kalman filters ; Long Term Evolution (LTE) ; Noise abatement ; Autoregressive modelling ; De-noising ; Drift reduction ; Gyro de-noising ; Long term error ; Long-term evolution ; Micro electro mechanical sensors ; Vehicle's dynamics ; Gyroscopes
  8. Source: IEEE Transactions on Instrumentation and Measurement ; Volume 71 , 2022 ; 00189456 (ISSN)
  9. URL: https://ieeexplore.ieee.org/abstract/document/9785621