Search research 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

Related Concept Videos

Linear Approximation in Time Domain

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...

Reconstruction of Signal using Interpolation

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...

Linear Approximation in Frequency Domain

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....

Sampling Continuous Time Signal

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...

Convergence of Fourier Series

Convergence of Fourier Series

The Fourier series is a powerful mathematical tool for representing periodic signals as an infinite sum of complex exponentials. In practice, this infinite series is truncated to a finite number of terms, yielding a partial sum. This truncation makes the approximation of the signal feasible but introduces certain challenges, particularly near discontinuities, known as the Gibbs phenomenon.
The Gibbs phenomenon refers to the persistent oscillations and overshoots that occur near discontinuities...

Sampling Theorem

Sampling Theorem

In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.

You might also read

Related Articles

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

Sort by

Same author

BO-LSTM-Based TDE Precise Estimation Model of Capacitive MEMS-Gyros Using Thermal-Induced Physical Characteristics Variation Analysis.

Micromachines·2026

Same author

An Improved Artificial Lemming Algorithm Integrating Non-Uniform Mutation and Q-Learning Adaptation for Underwater Manipulator Controller Tuning.

Biomimetics (Basel, Switzerland)·2026

Same author

Optimization of Structural Parameters and Mechanical Performance Analysis of a Novel Redundant Actuation Rehabilitation Training Robot.

Biomimetics (Basel, Switzerland)·2025

Same author

A Novel Temperature Drift Error Estimation Model for Capacitive MEMS Gyros Using Thermal Stress Deformation Analysis.

Micromachines·2024

Same author

A Novel Temperature Drift Error Precise Estimation Model for MEMS Accelerometers Using Microstructure Thermal Analysis.

Micromachines·2022

Same author

Modification of MTEA-Based Temperature Drift Error Compensation Model for MEMS-Gyros.

Sensors (Basel, Switzerland)·2020

Search research articles

Related Experiment Video

Updated: Jun 29, 2025

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

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

Published on: April 4, 2017

A Kalman Filtering Algorithm for Measurement Interruption Based on Polynomial Interpolation and Taylor Expansion.

Jianhua Cheng¹, Zili Wang¹, Bing Qi¹

¹College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China.

Entropy (Basel, Switzerland)

|March 28, 2024

Summary

This study introduces an adaptive Kalman filtering algorithm to improve navigation accuracy during GPS signal loss. The method enhances accuracy in attitude, velocity, and position estimation for combined SINS/GPS systems.

Keywords:

Taylor expansion adaptive filtering measurement interruption polynomial fitting virtual measurement

More Related Videos

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Published on: March 12, 2019

Experimental Methods to Study Human Postural Control

Experimental Methods to Study Human Postural Control

Published on: September 11, 2019

Related Experiment Videos

Last Updated: Jun 29, 2025

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

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

Published on: April 4, 2017

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Published on: March 12, 2019

Experimental Methods to Study Human Postural Control

Experimental Methods to Study Human Postural Control

Published on: September 11, 2019

Area of Science:

Navigation Systems Engineering
Signal Processing
Control Theory

Background:

Combined Strapdown Inertial Navigation System (SINS) and Global Positioning System (GPS) navigation systems are widely used but suffer from GPS signal blockage in urban canyons, tunnels, and under foliage.
GPS signal interruption causes SINS/GPS systems to degrade to pure inertial navigation, leading to significant accumulated errors.

Purpose of the Study:

To propose an adaptive Kalman filtering algorithm to mitigate navigation errors caused by GPS signal interruption in combined SINS/GPS systems.
To enhance the accuracy and stability of navigation solutions when GPS signals are temporarily unavailable.

Main Methods:

Developed an adaptive Kalman filtering algorithm incorporating polynomial fitting and Taylor expansion.
Utilized inertial guidance system data for polynomial interpolation to construct virtual velocity and position measurements during GPS outages.
Employed Taylor expansion to create virtual measurements, compensating for the lack of GPS data.

Main Results:

Computer simulations and road tests demonstrated improved performance compared to standard algorithms without rescue measures.
The proposed algorithm significantly enhanced accuracy in attitude angle estimation.
The algorithm also improved velocity estimation accuracy and positional localization precision.
The system exhibited higher overall stability during GPS signal interruptions.

Conclusions:

The proposed adaptive Kalman filtering algorithm effectively compensates for GPS signal loss in combined SINS/GPS navigation.
This method significantly improves navigation accuracy and system stability in challenging environments.
The technique offers a robust solution for maintaining reliable navigation performance when GPS is intermittently unavailable.