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

Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

20.2K
It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
20.2K
Two-Dimensional Force System01:20

Two-Dimensional Force System

1.7K
A two-dimensional system in mechanical engineering involves the analysis of motion and forces in a plane. A two-dimensional force vector can be resolved into its components as:
1.7K
Vector Components in the Cartesian Coordinate System01:29

Vector Components in the Cartesian Coordinate System

29.3K
Vectors are usually described in terms of their components in a coordinate system. Even in everyday life, we naturally invoke the concept of orthogonal projections in a rectangular coordinate system. For example, if someone gives you directions for a particular location, you will be told to go a few km in a direction like east, west, north, or south, along with the angle in which you are supposed to move. In a rectangular (Cartesian) xy-coordinate system in a plane, a point in a plane is...
29.3K
Direction Cosines of a Vector01:29

Direction Cosines of a Vector

1.7K
Direction cosines, which help describe the orientation of a vector with respect to the coordinate axes, are an essential concept in the field of vector calculus. Consider vector A that is expressed in terms of the Cartesian vector form using i, j, and k unit vectors. The magnitude of vector A is defined as the square root of the sum of the squares of its components. The direction of this vector with respect to the x, y, and z axes is defined by the coordinate direction angles α, β, and γ,...
1.7K
Two-Dimensional Force System: Problem Solving01:29

Two-Dimensional Force System: Problem Solving

1.4K
Solving problems related to two-dimensional force systems is an essential aspect of mechanics and engineering. By applying the principles of vector analysis and force equilibrium, one can determine the effect of multiple forces acting on an object in a two-dimensional space.
The first step to solving a two-dimensional force system problem is to draw a free-body diagram of the object under consideration. This diagram helps identify all the external forces acting on the object, including their...
1.4K
Derivatives of Inverse Trigonometric Functions01:30

Derivatives of Inverse Trigonometric Functions

451
A ship tracking an approaching aircraft relies on geometric measurements to find out the aircraft’s position relative to the observer. By measuring the slant distance to the aircraft and the angle of elevation, the horizontal and vertical components of the distance can be obtained using trigonometric relationships. This geometric approach provides a basis for analyzing how the observed angle changes as the aircraft moves closer to the ship.To examine the mathematical behavior of the angle...
451

You might also read

Related Articles

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

Sort by
Same author

Evaluation of the RTyper Y27 chip amplification system with Quick TargSeq 1.0 for Y-STR genotyping in forensic science.

International journal of legal medicine·2026
Same author

Combination of degrading with non-degrading endophytic bacteria synergistically enhances rice resistance to chlorpyrifos stress.

Journal of advanced research·2026
Same author

Rice recruiting resilience-conferring Enterobacter via enhanced proline and malic acid exudation under thiamethoxam stress.

Journal of hazardous materials·2026
Same author

Construction and validation of the prediction model for fear of cancer recurrence in patients with postoperative cervical cancer.

Frontiers in oncology·2025
Same author

Dual-Mode Strain Relief via Zinc Acetate Enables High-Efficiency InP Quantum Dot Light-Emitting Diodes.

Angewandte Chemie (International ed. in English)·2025
Same author

A high-yield xylanase platform for efficient xylooligosaccharides production from agricultural residues.

Bioresource technology·2025

Related Experiment Video

Updated: Mar 3, 2026

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

9.9K

A Type-2 Block-Component-Decomposition Based 2D AOA Estimation Algorithm for an Electromagnetic Vector Sensor Array.

Yu-Fei Gao1, Guan Gui2, Wei Xie3

  • 1School of Electronic Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave., West Hi-Tech Zone, Chengdu 611731, China. yufeiee@gmail.com.

Sensors (Basel, Switzerland)
|April 28, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel two-dimensional angle of arrival (2D AOA) estimation algorithm for electromagnetic vector sensor (EMVS) arrays using Type-2 block component decomposition (BCD). The method enhances accuracy and robustness, even in challenging low signal-to-noise ratio environments.

Keywords:
2D AOAEMVSL-shaped arraypartial polarizationrank-(L1, L2,·) BCDtensor decomposition

More Related Videos

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

8.2K
Frequency Mixing Magnetic Detection Scanner for Imaging Magnetic Particles in Planar Samples
07:01

Frequency Mixing Magnetic Detection Scanner for Imaging Magnetic Particles in Planar Samples

Published on: June 9, 2016

10.0K

Related Experiment Videos

Last Updated: Mar 3, 2026

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

9.9K
Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

8.2K
Frequency Mixing Magnetic Detection Scanner for Imaging Magnetic Particles in Planar Samples
07:01

Frequency Mixing Magnetic Detection Scanner for Imaging Magnetic Particles in Planar Samples

Published on: June 9, 2016

10.0K

Area of Science:

  • Signal Processing
  • Electromagnetics
  • Array Signal Processing

Background:

  • Electromagnetic vector sensor (EMVS) arrays are crucial for direction finding.
  • Existing tensor decomposition methods for EMVS arrays have limitations, including uniqueness conditions and handling partially-polarized signals.

Purpose of the Study:

  • To develop a robust 2D AOA estimation algorithm for L-shaped EMVS arrays.
  • To overcome limitations of existing tensor decomposition methods for partially-polarized signals.

Main Methods:

  • Utilizing Type-2 block component decomposition (BCD) tensor modeling for partially-polarized signals.
  • Developing a 2D AOA estimation algorithm based on rank-(L1, L2, ·) BCD.
  • Analyzing the uniqueness conditions of the BCD decomposition.

Main Results:

  • The proposed algorithm achieves automatic angle pair-matching via the estimated steering matrix.
  • Demonstrated superior accuracy and robustness in parameter estimation compared to subspace and CPD methods.
  • Maintained performance under low SNR, small angular separation, and limited snapshots.

Conclusions:

  • The Type-2 BCD tensor modeling provides an effective approach for 2D AOA estimation with EMVS arrays.
  • The developed algorithm offers significant advantages over existing methods, particularly for partially-polarized signals and challenging conditions.