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

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

7.1K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
7.1K
Determination of Expected Frequency01:08

Determination of Expected Frequency

2.3K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.3K
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

186
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
186

You might also read

Related Articles

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

Sort by
Same author

Stabilizing Iodine Redox Mediator Enables High-Performance Aqueous Zinc-Sulfur Batteries.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Efficacy and safety of chimeric antigen receptor T-cell therapy in relapsed/refractory large B-cell lymphoma: a systematic review and meta-analysis.

Therapeutic advances in hematology·2026
Same author

Interface Layer Engineering of Zinc Anode for Durable Seawater-Based Zinc-Ion Batteries.

Advanced materials (Deerfield Beach, Fla.)·2025
Same author

Plasmonic Double Perovskite LaSrCoMnO<sub>6</sub> Drives Efficient and Selective Photothermal Catalytic Styrene Epoxidation.

Nano letters·2025
Same author

Order-by-disorder in magnets with frustrated spin interactions-classical and large-<i>S</i>limits via the spin functional integral.

Journal of physics. Condensed matter : an Institute of Physics journal·2025
Same author

Exceptional Electroreduction of Nitrate to Ammonia Promoted by Concerted Electron-Proton Transfer in Cu-Decorated <i>MFI</i> Zeolites.

ACS applied materials & interfaces·2025

Related Experiment Video

Updated: Sep 21, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K

Robust PMBM Filter with Unknown Detection Probability Based on Feature Estimation.

Yi Wang1,2,3, Peng Rao1,2, Xin Chen1,2

  • 1Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China.

Sensors (Basel, Switzerland)
|May 28, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an improved multi-target tracking filter that estimates unknown detection probability using target features. This method enhances tracking accuracy in complex, real-world scenarios.

Keywords:
PMBM filterfeature estimationinverse gamma-gaussian mixtureunknown detection probability

More Related Videos

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

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

1.8K
Rapid Homogeneous Detection of Biological Assays Using Magnetic Modulation Biosensing System
06:58

Rapid Homogeneous Detection of Biological Assays Using Magnetic Modulation Biosensing System

Published on: June 13, 2010

9.7K

Related Experiment Videos

Last Updated: Sep 21, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

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

1.8K
Rapid Homogeneous Detection of Biological Assays Using Magnetic Modulation Biosensing System
06:58

Rapid Homogeneous Detection of Biological Assays Using Magnetic Modulation Biosensing System

Published on: June 13, 2010

9.7K

Area of Science:

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Robotics and Autonomous Systems

Background:

  • Standard Poisson Multi-Bernoulli Mixture (PMBM) filters assume known, prior detection probabilities.
  • Real-world multi-target tracking often involves time-varying and unknown detection probabilities due to sensor and environmental factors.
  • This limitation hinders the practical application of standard PMBM filters.

Purpose of the Study:

  • To develop an enhanced PMBM filter capable of handling unknown and time-varying detection probabilities.
  • To integrate target features into the state estimation to dynamically infer detection probability.
  • To validate the proposed filter's performance against existing methods in challenging tracking scenarios.

Main Methods:

  • Modeled target kinematic state using a Gaussian distribution.
  • Modeled target features using an inverse gamma distribution.
  • Integrated features into the kinematic state for iterative estimation of time-varying detection probability alongside motion state.

Main Results:

  • The proposed filter significantly outperformed the standard PMBM filter and a Beta distribution-based robust PMBM filter.
  • Demonstrated superior performance in scenarios characterized by unknown and time-varying detection probabilities.
  • Validated effectiveness and robustness through application to a simulated infrared image dataset.

Conclusions:

  • The developed filter effectively addresses the limitations of standard PMBM filters in practical multi-target tracking.
  • Integrating target features provides a robust mechanism for estimating unknown detection probabilities.
  • The proposed method offers a viable solution for real-world tracking applications with uncertain detection.