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

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...

You might also read

Related Articles

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

Sort by
Same author

Redefining structural stability in Ni-rich single-crystalline cathodes.

National science review·2026
Same author

A lightweight hybrid framework for real-time data refinement in resource-constrained underwater and underground wireless sensor networks.

Scientific reports·2026
Same author

Bispecific antibody vesicles: A multifunctional bioactive drug delivery platform for the treatment of <i>Pseudomonas aeruginosa</i> infection.

Asian journal of pharmaceutical sciences·2026
Same author

Levan-derived oligosaccharides (LOS) confer selective resistance against Phytophthora capsici but not Botrytis cinerea in melon and watermelon.

International journal of biological macromolecules·2026
Same author

Enhancing Efficiency and Stability of Perovskite Solar Cells Through Electron-Rich Covalent Organic Frameworks Radicals.

Angewandte Chemie (International ed. in English)·2026
Same author

Vaccine Effectiveness Estimates Against Influenza A(H3N2)-Associated Hospitalized Severe Acute Respiratory Infections in Beijing, China, 2025/26 Influenza Season.

Vaccines·2026
Same journal

Amplitude-invariant phase masking for coherence recovery in scattered wavefields.

JASA express letters·2026
Same journal

Detecting continuous and discrete frequency changes as a function of spectral resolvability and modulation rate.

JASA express letters·2026
Same journal

Bearings-only acoustic source localization method using two distributed gliders and deep ocean experimental validation in the South China Sea.

JASA express letters·2026
Same journal

Block-sparse enhancement and detection of envelope modulation on noise for ship radiated noise.

JASA express letters·2026
Same journal

Predicting acoustic field with a separate variable ocean physics-informed neural network.

JASA express letters·2026
Same journal

Extending Sottek Hearing Model loudness to estimate partially-masked sound qualities of loudness, tonality, and sharpness.

JASA express letters·2026
See all related articles

Related Experiment Video

Updated: Jun 28, 2026

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish
10:56

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Published on: March 6, 2014

12.5K

A modified adaptive Kalman filter algorithm for the distributed underwater multi-target passive tracking system.

Xuefei Ma1,2, Jiaxin Ma1, Zexu Ma2

  • 1College of Information Science and Technology, Tibet University, Lhasa, 850032, China.

JASA Express Letters
|January 2, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a modified adaptive Kalman filter (AKF) for reliable underwater multi-target tracking. The new AKF algorithm significantly improves accuracy in estimating target position and velocity, even with uncertain noise.

More Related Videos

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus
05:57

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Published on: April 8, 2019

6.8K
Behavioral Tracking and Neuromast Imaging of Mexican Cavefish
14:58

Behavioral Tracking and Neuromast Imaging of Mexican Cavefish

Published on: April 6, 2019

7.6K

Related Experiment Videos

Last Updated: Jun 28, 2026

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish
10:56

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Published on: March 6, 2014

12.5K
Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus
05:57

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Published on: April 8, 2019

6.8K
Behavioral Tracking and Neuromast Imaging of Mexican Cavefish
14:58

Behavioral Tracking and Neuromast Imaging of Mexican Cavefish

Published on: April 6, 2019

7.6K

Area of Science:

  • Robotics
  • Signal Processing
  • Navigation Systems

Background:

  • Underwater multi-target tracking presents challenges due to uncertain measurement noise.
  • Traditional Kalman filter algorithms struggle with dynamic and unpredictable noise environments.

Purpose of the Study:

  • To develop a robust and accurate algorithm for underwater multi-target tracking.
  • To enhance the reliability of tracking systems operating in environments with uncertain measurement noise.

Main Methods:

  • A modified adaptive Kalman filter (AKF) algorithm was developed.
  • Key modifications include an adaptive fading factor, measurement noise covariance adjustment, and an adaptive weighting factor.
  • The algorithm estimates unknown measurement noise and the state vector concurrently.

Main Results:

  • The proposed AKF algorithm demonstrated superior performance compared to traditional methods.
  • Achieved at least a 10.29% reduction in root-mean-square error (RMSE) for estimated position.
  • Achieved at least a 52.57% reduction in RMSE for estimated velocity.

Conclusions:

  • The modified AKF algorithm provides a reliable solution for underwater multi-target tracking.
  • The adaptive features enhance accuracy and robustness in the presence of uncertain measurement noise.
  • Empirical data validates the algorithm's significant performance improvements.