Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Doppler Effect - II01:05

Doppler Effect - II

5.0K
The Doppler effect has several practical, real-world applications. For instance, meteorologists use Doppler radars to interpret weather events based on the Doppler effect. Typically, a transmitter emits radio waves at a specific frequency toward the sky from a weather station. The radio waves bounce off the clouds and precipitation and travel back to the weather station. The radio frequency of the waves reflected back to the station appears to decrease if the clouds or precipitation are moving...
5.0K
Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

860
A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
When an external force is exerted, it sets the crank into a rotational movement. This, in turn, instigates the motion of the connecting rod, leading to what is referred to as a general plane motion. This process involves two key points - point A on the connecting rod...
860
Doppler Effect - I00:56

Doppler Effect - I

6.7K
The Doppler effect and Doppler shift were named after the Austrian physicist and mathematician Christian Johann Doppler in 1842, who conducted experiments with both moving sources and moving observers. Consider an observer standing on a street corner, observing an ambulance with a siren sound passing by at a constant speed. The observer experiences two characteristic changes in the sound of the siren. Initially, the sound increases in loudness as the ambulance approaches and decreases in...
6.7K
Drift Velocity01:19

Drift Velocity

5.8K
The high speed of electrical signals results from the fact that the force between charges acts rapidly at a distance. Thus, when a free charge is forced into a wire, the incoming charge pushes other charges ahead due to the repulsive force between like charges. These moving charges move the charges farther down the line. The density of charge in a system cannot easily be increased, so the signal is passed on rapidly. The resulting electrical shock wave moves through the system at nearly the...
5.8K
Velocity and Position by Integral Method01:13

Velocity and Position by Integral Method

8.7K
If acceleration as a function of time is known, then velocity and position functions can be derived using integral calculus. For constant acceleration, the integral equations refer to the first and second kinematic equations for velocity and position functions, respectively.
Consider an example to calculate the velocity and position from the acceleration function. A motorboat is traveling at a constant velocity of 5.0 m/s when it starts to decelerate to arrive at the dock. Its acceleration is...
8.7K
Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

979
A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
979

You might also read

Related Articles

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

Sort by
Same author

MoRPI-PINN: a physics-informed framework for mobile robot pure inertial navigation.

Scientific reports·2026
Same author

Canine gait analysis using inertial sensors and deep learning for orthopedic and neurological disorders.

Scientific reports·2026
Same author

Wheel-Mounted Inertial Datasets.

Scientific data·2025
Same author

CRATER tumor niches facilitate CD8<sup>+</sup> T cell engagement and correspond with immunotherapy success.

Cell·2025
Same author

Snake-inspired mobile robot positioning with hybrid learning.

Scientific reports·2025
Same author

Multiple and Gyro-Free Inertial Datasets.

Scientific data·2024
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Mar 7, 2026

Quantitatively Measuring In situ Flows using a Self-Contained Underwater Velocimetry Apparatus SCUVA
09:22

Quantitatively Measuring In situ Flows using a Self-Contained Underwater Velocimetry Apparatus SCUVA

Published on: October 31, 2011

13.5K

Inertial Navigation System/Doppler Velocity Log (INS/DVL) Fusion with Partial DVL Measurements.

Asaf Tal1,2, Itzik Klein3, Reuven Katz4

  • 1Faculty of Mechanical Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel. asafta@rafael.co.il.

Sensors (Basel, Switzerland)
|March 1, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces an extended loosely coupled (ELC) approach to improve navigation for autonomous underwater vehicles (AUVs) when Doppler Velocity Log (DVL) data is incomplete. The ELC method enhances navigation accuracy by utilizing partial DVL data, mitigating drift in AUV systems.

Keywords:
Doppler velocity log/inertial navigation system (DVL/INS) fusionautonomous underwater vehicle six degrees of freedom (AUV 6DOF) simulationloosely coupledpartial measurementtightly coupled

More Related Videos

A Novel Application of Musculoskeletal Ultrasound Imaging
10:53

A Novel Application of Musculoskeletal Ultrasound Imaging

Published on: September 17, 2013

24.7K
Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
10:53

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Published on: March 12, 2019

7.7K

Related Experiment Videos

Last Updated: Mar 7, 2026

Quantitatively Measuring In situ Flows using a Self-Contained Underwater Velocimetry Apparatus SCUVA
09:22

Quantitatively Measuring In situ Flows using a Self-Contained Underwater Velocimetry Apparatus SCUVA

Published on: October 31, 2011

13.5K
A Novel Application of Musculoskeletal Ultrasound Imaging
10:53

A Novel Application of Musculoskeletal Ultrasound Imaging

Published on: September 17, 2013

24.7K
Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
10:53

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Published on: March 12, 2019

7.7K

Area of Science:

  • Robotics
  • Ocean Engineering
  • Navigation Systems

Background:

  • Autonomous Underwater Vehicles (AUVs) require robust navigation systems for research missions.
  • Inertial Navigation Systems (INS) are commonly used but prone to drift.
  • Doppler Velocity Logs (DVLs) aid INS but can fail, leading to navigation errors.

Purpose of the Study:

  • To develop an improved navigation approach for the Technion Autonomous Underwater Vehicle (TAUV).
  • To address navigation solution drift caused by partial or failed Doppler Velocity Log (DVL) measurements.
  • To propose an Extended Loosely Coupled (ELC) integration method for enhanced AUV navigation.

Main Methods:

  • Implementing an Extended Loosely Coupled (ELC) approach using partial raw DVL data and additional information.
  • Modifying the navigation system through software updates only.
  • Developing and utilizing a six degrees of freedom (6DOF) simulation of the TAUV, including all functional subsystems.

Main Results:

  • The proposed ELC approach effectively utilizes partial DVL measurements to provide a reliable velocity solution.
  • The simulation demonstrated a significant reduction in navigation solution drift compared to traditional loosely coupled methods.
  • The ELC approach requires only software modifications, making it practical for existing AUV systems.

Conclusions:

  • The Extended Loosely Coupled (ELC) approach offers a viable solution to enhance AUV navigation accuracy during DVL measurement outages.
  • The developed simulation validates the effectiveness of the ELC method in mitigating navigation drift.
  • This software-based enhancement provides a practical and cost-effective way to improve AUV navigation performance.