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

Poisson Probability Distribution01:09

Poisson Probability Distribution

10.6K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
10.6K
Poisson's And Laplace's Equation01:25

Poisson's And Laplace's Equation

3.7K
The electric potential of the system can be calculated by relating it to the electric charge densities that give rise to the electric potential. The differential form of Gauss's law expresses the electric field's divergence in terms of the electric charge density.
3.7K
Probability Histograms01:17

Probability Histograms

12.6K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
12.6K
Bernoulli's Equation00:59

Bernoulli's Equation

11.6K
In the middle of the nineteenth century, it was observed that two trains passing each other at a high relative speed get pulled towards each other. The same occurs when two cars pass each other at a high relative speed. The reason is that the fluid pressure drops in the region where the fluid speeds up. As the air between the trains or the cars increases in speed, its pressure reduces. The pressure on the outer parts of the vehicles is still the atmospheric pressure, while the resultant...
11.6K
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

182
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...
182
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

238
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
238

You might also read

Related Articles

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

Sort by
Same author

Factors associated with accepting chemotherapy despite the risk of fertility loss in Latin American breast cancer patients-LACOG 0414 study.

Therapeutic advances in medical oncology·2025
Same author

Perception of stochastically undersampled sound waveforms: a model of auditory deafferentation.

Frontiers in neuroscience·2013
See all related articles

Related Experiment Video

Updated: Nov 3, 2025

Image-based Lagrangian Particle Tracking in Bed-load Experiments
10:32

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Published on: July 20, 2017

9.1K

Space Debris Tracking with the Poisson Labeled Multi-Bernoulli Filter.

Leonardo Cament1, Martin Adams1, Pablo Barrios1

  • 1Department of Electrical Engineering, Universidad de Chile, Av. Tupper 2007, Santiago 8370451, Chile.

Sensors (Basel, Switzerland)
|June 2, 2021
PubMed
Summary

This study introduces a Bayesian filter for tracking space objects (SOs) using optical telescope data. The novel approach enhances multi-object tracking accuracy, even with limited observations, improving space situational awareness.

Keywords:
Poisson labeled multi-Bernoulli filtermulti-target trackingrandom finite setsspace situational awareness

More Related Videos

A Protocol for Real-time 3D Single Particle Tracking
10:16

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

15.1K
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.5K

Related Experiment Videos

Last Updated: Nov 3, 2025

Image-based Lagrangian Particle Tracking in Bed-load Experiments
10:32

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Published on: July 20, 2017

9.1K
A Protocol for Real-time 3D Single Particle Tracking
10:16

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

15.1K
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.5K

Area of Science:

  • Astronautics and Space Science
  • Data Science and Machine Learning
  • Applied Mathematics

Background:

  • Accurate tracking of space objects (SOs) is crucial for space situational awareness and collision avoidance.
  • Existing multi-object tracking methods face challenges with sparse data and complex orbital dynamics.
  • Bayesian filtering offers a probabilistic framework for state estimation in dynamic systems.

Purpose of the Study:

  • To develop and evaluate a Bayesian filter-based solution for multi-space object tracking.
  • To address the challenges of tracking low Earth orbit (LEO) objects with limited observational data.
  • To improve the accuracy and robustness of space object tracking algorithms.

Main Methods:

  • Utilized the Probabilistic Admissible Region (PAR) approach for orbital state estimation.
  • Implemented a Poisson Labeled Multi-Bernoulli (PLMB) multi-target tracking filter.
  • Employed the Partially Uniform Birth (PUB) intensity model for multi-target birth density.
  • Simulated optical telescopic observations from a network of twelve telescopes.

Main Results:

  • Demonstrated encouraging multi-SO tracking results using simulated trajectories from Two-Line Element (TLE) data.
  • Achieved effective tracking performance even with very low numbers of observations per SO pass.
  • Validated the proposed PLMB filter with PAR and PUB models using OSPA and CLEAR MOT metrics.

Conclusions:

  • The proposed Bayesian filter offers a robust solution for multi-space object tracking in LEO.
  • The PAR approach effectively incorporates physical constraints into the tracking process.
  • The PLMB filter with PUB intensity shows promise for enhancing space situational awareness with sparse data.