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

Probability Laws01:49

Probability Laws

44.5K
Overview
44.5K
Probability Distributions01:32

Probability Distributions

12.2K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
12.2K
Probability in Statistics01:14

Probability in Statistics

23.6K
Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
23.6K
Probability Histograms01:17

Probability Histograms

13.3K
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.
13.3K
Passive Filters01:27

Passive Filters

1.0K
Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
Low-Pass Filters
Low-pass filters are designed to transmit signals with frequencies lower than the cutoff frequency, ωc, and attenuate those above it. The cutoff...
1.0K
Active Filters01:25

Active Filters

1.4K
Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
1.4K

You might also read

Related Articles

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

Sort by
Same author

A Simulation Framework for Zoom-Aided Coverage Path Planning with UAV-Mounted PTZ Cameras.

Sensors (Basel, Switzerland)·2025
Same author

Securing UAV Flying Ad Hoc Wireless Networks: Authentication Development for Robust Communications.

Sensors (Basel, Switzerland)·2025
Same author

A Probabilistic-Geometric Approach for UAV Detection and Avoidance Systems.

Sensors (Basel, Switzerland)·2022
Same author

Consensus Tracking of Nonlinear Agents Using Distributed Nonlinear Dynamic Inversion with Switching Leader-Follower Connection.

Sensors (Basel, Switzerland)·2022
Same author

Defects Recognition Algorithm Development from Visual UAV Inspections.

Sensors (Basel, Switzerland)·2022
Same author

Bipartite Consensus of Nonlinear Agents in the Presence of Communication Noise.

Sensors (Basel, Switzerland)·2022
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: Feb 15, 2026

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

15.4K

Joint Probabilistic Data Association Filter with Unknown Detection Probability and Clutter Rate.

Shaoming He1, Hyo-Sang Shin2, Antonios Tsourdos3

  • 1School of Aerospace, Transport and Manufacturing, Cranfield University, MK43 0AL Cranfield, UK. Shaoming.He@cranfield.ac.uk.

Sensors (Basel, Switzerland)
|January 19, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new joint probabilistic data association (JPDA) filter for multi-object tracking when detection probability and clutter rate are unknown. The novel filter demonstrates performance comparable to ideal JPDA, making it practical for real-world applications.

Keywords:
joint probabilistic data associationmulti-Bernoulli filtermultiple target trackingunknown clutter rateunknown detection probability

More Related Videos

Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat
11:18

Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat

Published on: September 12, 2014

15.8K
Electrochemical Impedance Spectroscopy as a Tool for Electrochemical Rate Constant Estimation
08:41

Electrochemical Impedance Spectroscopy as a Tool for Electrochemical Rate Constant Estimation

Published on: October 10, 2018

25.9K

Related Experiment Videos

Last Updated: Feb 15, 2026

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

15.4K
Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat
11:18

Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat

Published on: September 12, 2014

15.8K
Electrochemical Impedance Spectroscopy as a Tool for Electrochemical Rate Constant Estimation
08:41

Electrochemical Impedance Spectroscopy as a Tool for Electrochemical Rate Constant Estimation

Published on: October 10, 2018

25.9K

Area of Science:

  • Engineering
  • Computer Science
  • Signal Processing

Background:

  • Multi-object tracking is crucial in various applications.
  • Accurate estimation of detection probability and clutter rate is challenging.
  • Existing methods often assume known environmental parameters.

Purpose of the Study:

  • To develop a novel joint probabilistic data association (JPDA) filter for robust multi-object tracking.
  • To address the challenge of unknown detection probability and clutter rate.
  • To enable practical implementation in diverse applications.

Main Methods:

  • A two-part algorithm combining a standard JPDA filter with a Poisson point process birth model.
  • Integration of a multi-Bernoulli filter for estimating unknown detection probability and clutter rate.
  • Empirical performance evaluation through simulation and testing.

Main Results:

  • The proposed JPDA filter achieves performance comparable to an ideal JPDA filter with known parameters.
  • The algorithm effectively handles unknown detection probability and clutter rate.
  • Empirical tests validate the filter's effectiveness and practicality.

Conclusions:

  • The developed JPDA filter offers a practical solution for multi-object tracking in uncertain environments.
  • The algorithm's ability to estimate unknown parameters enhances its applicability.
  • This work contributes to advancing robust target tracking technologies.