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 Experiment Video

Updated: Jul 19, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Integrated approach for automatic target recognition using a network of collaborative sensors.

Abhijit Mahalanobis1, Alan Van Nevel

  • 1Missiles and Fire Control, Lockheed Martin, Orlando, Florida 32819, USA. abhijit.mahalanobis@lmco.com

Applied Optics
|September 20, 2006
PubMed
Summary

This study presents a novel collaborative approach for object recognition using multiple sensors with automatic target recognition (ATR) capabilities. This method optimizes sensor placement and interactions for enhanced performance in netted systems.

Related Concept Videos

Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...

You might also read

Related Articles

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

Sort by
Same author

Efficient Drone Detection Using Temporal Anomalies and Small Spatio-Temporal Networks.

Sensors (Basel, Switzerland)·2026
Same author

Simultaneous Classification of Objects with Unknown Rejection (SCOUR) Using Infra-Red Sensor Imagery.

Sensors (Basel, Switzerland)·2025
Same author

Detection and Localization of Small Moving Objects in the Presence of Sensor and Platform Movement.

Sensors (Basel, Switzerland)·2024
Same author

Three-dimensional integral imaging and object detection using long-wave infrared imaging.

Applied optics·2017
Same author

Compressive Sensing Focus Issue: introduction.

Applied optics·2015
Same author

Optimizing measurements for feature-specific compressive sensing.

Applied optics·2014

Area of Science:

  • Sensor networks
  • Artificial intelligence
  • Signal processing

Background:

  • Current sensor systems often operate independently, limiting overall performance.
  • Netted systems offer potential for enhanced situational awareness through coordination.
  • Automatic target recognition (ATR) is crucial for identifying objects in complex environments.

Purpose of the Study:

  • To introduce a novel collaborative ATR concept for netted sensor systems.
  • To propose a self-configuring geometry for optimal sensor placement and interaction.
  • To demonstrate performance optimization through coordinated sensor-object recognition.

Main Methods:

  • Utilizing correlation filtering techniques for ATR algorithm development.
  • Developing a self-configuring geometry for netted platforms.

Related Experiment Videos

Last Updated: Jul 19, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

  • Analyzing sensor-algorithm-object interactions for optimal positioning.
  • Employing viewing position as a key sensor parameter for target recognition.

    • Main Results:

    • Demonstrated optimized overall performance in collaborative ATR.
    • Successfully illustrated the collaborative ATR scheme using synthetic aperture radar (SAR) imagery.
    • Validated the effectiveness of coordinated sensor networks for object recognition.

    Conclusions:

    • Collaborative ATR in netted systems offers significant performance advantages.
    • Optimal sensor configuration and interaction are key to maximizing recognition accuracy.
    • The proposed concept is adaptable to various ATR algorithms and sensor types.