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Related Concept Videos

Random Error01:04

Random Error

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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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A gyroscope is defined as a spinning disk in which the axis of rotation is free to assume any orientation. When spinning, the orientation of the spin axis is unaffected by the orientation of the body that encloses it. The body or vehicle enclosing the gyroscope can be moved from place to place, while the orientation of the spin axis remains the same. This makes gyroscopes very useful in navigation, especially where magnetic compasses cannot be used, such as in crewed and crewless spacecraft,...
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The deviations show how spread out the data are about the mean. A positive deviation occurs when the data value exceeds the mean, whereas a negative deviation occurs when the data value is less than the mean. If the deviations are added, the sum is always zero. So one cannot simply add the deviations to get the data spread. By squaring the deviations, the numbers are made positive; thus, their sum will also be positive.
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An Adaptive Filtering Approach Based on the Dynamic Variance Model for Reducing MEMS Gyroscope Random Error.

Yanshun Zhang1, Chuang Peng2, Dong Mou3

  • 1School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China. zhangyanshun@buaa.edu.cn.

Sensors (Basel, Switzerland)
|November 17, 2018
PubMed
Summary

This study introduces an adaptive filtering method to enhance Micro Electro Mechanical System (MEMS) gyroscope accuracy. The approach effectively compensates for dynamic random errors across various angular rates, improving overall performance.

Keywords:
Kalman FilterMEMS gyroscopedynamic random errorvariance model

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Area of Science:

  • * Instrumentation and Measurement
  • * Signal Processing
  • * Control Systems Engineering

Background:

  • * Micro Electro Mechanical System (MEMS) gyroscopes are crucial for inertial navigation but suffer from dynamic random errors.
  • * Existing compensation methods often struggle with varying angular rates, limiting their effectiveness.
  • * Accurate compensation requires understanding the relationship between gyroscope output variance and angular rate.

Purpose of the Study:

  • * To develop an adaptive filtering approach for improving MEMS gyroscope dynamic random error compensation accuracy.
  • * To establish a dynamic variance model that captures the nonlinear relationship between gyroscope output variance and angular rate.
  • * To enable online adjustment of Kalman Filter parameters for real-time error reduction.

Main Methods:

  • * Utilized experimental data to fit a dynamic variance model for MEMS gyroscopes.
  • * Applied the dynamic variance model to dynamically adjust Kalman Filter measurement noise coefficients.
  • * Implemented an adaptive filtering strategy for real-time error compensation.

Main Results:

  • * The proposed adaptive filtering approach effectively suppressed angular rate interference in filtering results.
  • * Dynamic random errors of the MEMS gyroscope were more accurately estimated and reduced.
  • * Turntable experiments confirmed effective compensation under both constant and changing angular rate conditions.

Conclusions:

  • * The adaptive filtering approach based on a dynamic variance model significantly enhances MEMS gyroscope dynamic random error compensation.
  • * This method provides effective and adaptive error compensation across diverse operational angular rates.
  • * The study achieves improved accuracy and reliability for MEMS gyroscope applications.