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

Time and frequency -Domain Interpretation of Phase-lead Control

Time and frequency -Domain Interpretation of Phase-lead Control

Phase-lead controllers are commonly used in various control systems to enhance response speed and stability. Adjusting the brightness on a television screen offers a practical example of phase-lead control. When contrast is enhanced, a phase-lead controller is employed. Mathematically, phase-lead control is identified when the first parameter is smaller than the second.
The design of phase-lead control involves the strategic placement of poles and zeros to balance steady-state error and system...

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.

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, the...

Calibration Curves: Linear Least Squares

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...

Application of Linearization and Approximation

Application of Linearization and Approximation

A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...

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 sampling...

You might also read

Related Articles

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

Sort by

Same author

Digital health literacy among university students: a systematic review of global trends, determinants, measurement, and the literacy-to-intervention gap.

BMC public health·2026

Same author

Fixation Strategies in Hoffa Fractures: A Systematic Review and Meta-Analysis of Surgical Outcomes.

Indian journal of orthopaedics·2026

Same author

Laparoscopic vs. robot-assisted pyeloplasty: a single-centre outcome analysis with 11 years median follow-up.

World journal of urology·2026

Same author

A Prospective Study of Steerable Ureteroscopic Renal Evacuation Using the Second-Generation CVAC® Aspiration System: Results from the CLEARANCE Study.

Journal of endourology·2026

Same author

Understanding meningococcal X unconjugated saccharide behaviour in monovalent and pentavalent (A, C, Y, W and X) conjugate formulations for quality analysis of meningococcal conjugate vaccine.

Carbohydrate research·2025

Same author

Surgical Complications in ABO-incompatible Kidney Transplantation: A Single-Center Experience.

Transplantation proceedings·2025

Search research articles

Related Experiment Video

Updated: Jun 23, 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

Phase-step estimation in interferometry via an unscented Kalman filter.

Thomas E Zander¹, Venkatesh Madyastha, Abhijit Patil

¹Optical + Biomedical Engineering Laboratory, School of Electrical, Electronic and Computer Engineering, University of Western Australia, Crawley WA 6009, Australia.

|May 5, 2009

Summary

This study introduces an unscented Kalman filter for precise phase step identification in phase shifting interferometry, even with Gaussian noise. The method calibrates piezoelectric transducers without prior device calibration, enabling accurate phase estimation.

More Related Videos

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

PyOKR: A Semi-Automated Method for Quantifying Optokinetic Reflex Tracking Ability

PyOKR: A Semi-Automated Method for Quantifying Optokinetic Reflex Tracking Ability

Published on: April 12, 2024

Related Experiment Videos

Last Updated: Jun 23, 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

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

PyOKR: A Semi-Automated Method for Quantifying Optokinetic Reflex Tracking Ability

PyOKR: A Semi-Automated Method for Quantifying Optokinetic Reflex Tracking Ability

Published on: April 12, 2024

Area of Science:

Optical Metrology
Signal Processing
Control Systems

Background:

Phase shifting interferometry (PSI) is a key technique for precise optical surface measurement.
Accurate determination of phase steps is crucial for reliable PSI measurements.
Piezoelectric transducers are commonly used for phase shifting but require calibration.

Purpose of the Study:

To develop a novel algorithm for identifying phase steps in PSI without prior calibration.
To address the challenge of Gaussian noise in phase step determination.
To enable accurate interference phase estimation using the identified phase steps.

Main Methods:

An unscented Kalman filter (UKF) algorithm was developed.
The UKF was applied to identify phase steps imparted to a piezoelectric transducer.
Simulated data with Gaussian noise and experimental holographic interferometry data were used for validation.

Main Results:

The proposed UKF successfully identified phase step values between -pi and pi radians.
The algorithm demonstrated robustness in the presence of Gaussian noise.
Experimental validation confirmed the effectiveness of the UKF approach in a holographic interferometry setup.

Conclusions:

The unscented Kalman filter provides an effective, calibration-free method for phase step identification in PSI.
This approach enhances the accuracy and reliability of interference phase estimation.
The method is suitable for optical metrology applications dealing with noisy data.