Search research articles

Related Concept Videos

Random and Systematic Errors

Random and Systematic Errors

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...

Random Error

Random Error

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Optimal Low-Cost MEMS INS/GNSS Integrated Georeferencing Solution for LiDAR Mobile Mapping Applications.

Sensors (Basel, Switzerland)·2025

See all related articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: May 25, 2026

Using Micro-Electro-Mechanical Systems (MEMS) to Develop Diagnostic Tools

Using Micro-Electro-Mechanical Systems (MEMS) to Develop Diagnostic Tools

Published on: October 1, 2007

A Rigorous Temperature-Dependent Stochastic Modelling and Testing for MEMS-Based Inertial Sensor Errors.

Mohammed El-Diasty¹, Spiros Pagiatakis

¹Department of Earth and Space Science and Engineering, York University, Toronto, ON M3J 1P3, Canada;

Sensors (Basel, Switzerland)

|February 1, 2012

Summary

This summary is machine-generated.

Temperature significantly impacts Micro-Electro-Mechanical Systems (MEMS) inertial sensor errors. Developing temperature-aware stochastic models is crucial for accurate navigation solutions in MEMS-based Inertial Navigation System/Global Positioning System (INS/GPS) integration.

Keywords:

AR model GM model MEMS UKF inertial sensor temperature

More Related Videos

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters

Published on: February 4, 2018

Related Experiment Videos

Last Updated: May 25, 2026

Using Micro-Electro-Mechanical Systems (MEMS) to Develop Diagnostic Tools

Using Micro-Electro-Mechanical Systems (MEMS) to Develop Diagnostic Tools

Published on: October 1, 2007

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters

Published on: February 4, 2018

Area of Science:

Sensor Technology
Navigation Systems
Stochastic Modeling

Background:

Micro-Electro-Mechanical Systems (MEMS) inertial sensors are vital components in navigation systems.
Sensor performance, particularly random error, can be affected by environmental factors like temperature.
Accurate modeling of sensor errors is essential for reliable navigation solutions.

Purpose of the Study:

To investigate the influence of varying temperature points on the random error of MEMS-based inertial sensors.
To develop and validate stochastic models that account for temperature-dependent sensor behavior.
To assess the impact of these models on the accuracy of navigation solutions.

Main Methods:

Static data collection from a MEMS inertial sensor (ADIS16364) across different temperatures within a thermal chamber.
Development of Autoregressive-based Gauss-Markov (AR-based GM) stochastic models to characterize random error.
Application of Unscented Kalman Filter (UKF) to test the performance of temperature-calibrated stochastic models using kinematic data.

Main Results:

Stochastic model parameters (correlation times) for the MEMS inertial unit were found to be temperature-dependent.
A stochastic model developed at 20 °C yielded a more accurate inertial navigation solution compared to models developed at other tested temperatures (-40 °C to +60 °C).
The temperature dependence of the stochastic model significantly affects navigation accuracy.

Conclusions:

The random error behavior of MEMS-based inertial sensors is demonstrably influenced by temperature.
Temperature-dependent stochastic modeling is a critical factor for optimizing MEMS-based INS/GPS integration.
Accurate navigation solutions necessitate continuous consideration of sensor temperature variations in stochastic models.