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

What are Estimates?01:06

What are Estimates?

8.8K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
8.8K
Estimation of k and VD of Aminoglycosides01:20

Estimation of k and VD of Aminoglycosides

246
Aminoglycosides are a class of antibiotics used to treat various bacterial infections. Clinicians must determine the elimination rate constant (k) and volume of distribution (VD) to optimize therapeutic efficacy and minimize toxicity. The k value represents the rate at which the drug is removed from the body, and the VD reflects the degree to which the drug distributes into body tissues. Accurately estimating these parameters allows healthcare professionals to tailor drug dosing to individual...
246
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

7.7K
On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
7.7K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.4K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.4K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

9.7K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
9.7K
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

8.9K
A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
8.9K

You might also read

Related Articles

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

Sort by
Same author

Error-Constrained Entropy-Minimizing Strategies for Multi-UAV Deception Against Networked Radars.

Entropy (Basel, Switzerland)·2025
Same author

Working Mode Recognition of Non-Specific Radar Based on ResNet-SVM Learning Framework.

Sensors (Basel, Switzerland)·2023
Same author

Application of time-reversal in electromagnetic power synthesis under distributed motion platform.

Heliyon·2022
Same author

An End-to-End Deep Learning Approach for State Recognition of Multifunction Radars.

Sensors (Basel, Switzerland)·2022
Same author

Recognition of Noisy Radar Emitter Signals Using a One-Dimensional Deep Residual Shrinkage Network.

Sensors (Basel, Switzerland)·2021
Same author

Hough Transform-Based Large Dynamic Reflection Coefficient Micro-Motion Target Detection in SAR.

Sensors (Basel, Switzerland)·2019
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 3, 2026

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

958

Two-Dimensional Angle Estimation of Two-Parallel Nested Arrays Based on Sparse Bayesian Estimation.

Lu Chen1, Daping Bi2, Jifei Pan3

  • 1Electronic Countermeasures College, National University of Defense Technology, Hefei 230037, China. chenluzhanjing@126.com.

Sensors (Basel, Switzerland)
|October 24, 2018
PubMed
Summary
This summary is machine-generated.

A novel two-parallel nested array design enhances signal source estimation. This new method enables accurate two-dimensional direction finding, significantly outperforming the number of sensors used.

Keywords:
decoupled estimationdegrees of freedomdirection of arrival estimationsparse Bayesian learningsparse arrays

More Related Videos

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.5K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

2.1K

Related Experiment Videos

Last Updated: Feb 3, 2026

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

958
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.5K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

2.1K

Area of Science:

  • Signal Processing
  • Array Signal Processing
  • Electromagnetics

Background:

  • Estimating multiple signal sources in complex environments is challenging.
  • Traditional direction of arrival (DOA) methods face limitations in source number capacity.
  • Advanced array configurations are needed for improved DOA estimation performance.

Purpose of the Study:

  • To propose a two-parallel nested array configuration for enhanced DOA estimation.
  • To develop a robust 2-D DOA estimation algorithm for the proposed array.
  • To increase the number of simultaneously estimable signal sources beyond sensor count.

Main Methods:

  • A two-parallel nested array architecture comprising two subarrays.
  • A sparse Bayesian estimation algorithm for 2-D DOA.
  • Techniques including vectorization, smoothing reconstruction, and SVD to reduce dictionary size and noise.
  • Sparse Bayesian learning for one-dimensional angle estimation.
  • Joint covariance matrix for the second-dimensional angle estimation and automatic pairing.

Main Results:

  • The proposed two-parallel nested arrays significantly increase the number of estimable signal sources.
  • The number of estimated DOA signals surpasses the number of sensors.
  • The 2-D DOA estimation algorithm demonstrates excellent performance and accuracy.
  • Automatic pairing of estimated angles across dimensions is achieved.

Conclusions:

  • The two-parallel nested array is an effective configuration for high-capacity DOA estimation.
  • The sparse Bayesian algorithm provides a robust solution for 2-D DOA problems with this array.
  • This approach offers superior performance in estimating multiple signal directions.