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Related Experiment Video

Updated: Jan 17, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Adaptive filter parameter reconstruction technology for rocket inertial navigation/satellite integrated navigation

Zhijie Yang¹, Guoguang Chen¹, Mingli Niu^1,2

¹College of Mechanical and Electrical Engineering, North University of China, Taiyuan, China.

Peerj. Computer Science

|September 24, 2025

Summary

This study introduces an Adaptive Reconfigurable Extended Kalman Filter (AREKF) for micro-electro-mechanical systems (MEMS) strapdown inertial navigation systems (SINS)/global navigation satellite systems (GNSS) integrated navigation. The AREKF enhances real-time navigation accuracy for rockets under high overload conditions.

Keywords:

Adaptive parameters Extended Kalman filter Guided rockets Integrated navigation MEMS-SINS/GNSS

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Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters

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Last Updated: Jan 17, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

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Published on: October 28, 2022

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

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Published on: April 4, 2017

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:

Navigation Systems Engineering
Aerospace Engineering
Signal Processing

Background:

Micro-electro-mechanical systems (MEMS) strapdown inertial navigation systems (SINS) integrated with global navigation satellite systems (GNSS) offer compact, affordable, high-precision navigation.
Rocket-borne MEMS-SINS/GNSS systems require high overload, accuracy, and real-time performance, posing challenges due to changing MEMS noise and dynamic flight environments.
Traditional Kalman filtering methods struggle with adaptive parameter modeling for real-time navigation under high overload.

Purpose of the Study:

To develop an advanced filtering method for MEMS-SINS/GNSS integrated navigation systems on rockets.
To address the challenges of noise variations and real-time demands in rocket flight navigation.
To improve the accuracy and real-time capability of navigation solutions under high overload conditions.

Main Methods:

Development of a precise system state model tailored to rocket flight dynamics.
Implementation of real-time filter parameter reconstruction during the rocket alignment phase.
Introduction of the Adaptive Reconfigurable Extended Kalman Filter (AREKF) algorithm.

Main Results:

The AREKF method demonstrates rapid convergence of the filtering process.
Adaptive modeling of navigation parameters ensures lower computational costs and enhanced accuracy.
AREKF significantly improves real-time navigation accuracy compared to traditional EKF and other improved algorithms in high overload scenarios.

Conclusions:

The AREKF method provides a robust solution for high-overload, real-time navigation in rocket-borne MEMS-SINS/GNSS systems.
This approach effectively models changing noise characteristics and dynamic flight environments.
AREKF enhances navigation precision and real-time output, validated through simulations and experiments.