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Related Concept Videos

Bearings: Problem Solving01:24

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Understanding the calculations and concepts related to double-collar bearings is essential for engineers and designers to optimize the performance of these components in various applications. By analyzing the bearing under different conditions, one can ensure that it can withstand the forces and moments experienced during operation. This knowledge enables better decision-making when designing and selecting bearings for specific purposes and configurations. Consider a double-collar bearing with...
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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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Rolling Resistance: Problem Solving01:17

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BOTNet: Deep Learning-Based Bearings-Only Tracking Using Multiple Passive Sensors.

Hadar Shalev1, Itzik Klein1

  • 1Department of Marine Technologies, University of Haifa, Haifa 3498838, Israel.

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|July 2, 2021
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Summary
This summary is machine-generated.

This study introduces a deep learning framework for bearings-only target tracking, outperforming traditional iterative least squares methods. The new approach enhances accuracy in tracking applications like autonomous underwater vehicle localization.

Keywords:
autonomous underwater vehiclebearings-onlydeep learningtarget tracking

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Area of Science:

  • Robotics and Control Systems
  • Signal Processing
  • Artificial Intelligence

Background:

  • Bearings-only target tracking is crucial for navigation, surveillance, and military operations.
  • Traditional methods like iterative least squares rely on line-of-sight measurements from passive sensors.
  • These methods can be computationally intensive and may struggle with complex scenarios.

Purpose of the Study:

  • To develop and evaluate a novel deep-learning-based framework for bearings-only target tracking.
  • To demonstrate the framework's applicability across various tracking tasks.
  • To compare the deep learning approach against conventional iterative least squares algorithms.

Main Methods:

  • A data-driven deep learning framework was designed for bearings-only target tracking.
  • The framework processes line-of-sight measurements from passive multisensor systems.
  • A specific scenario involving an autonomous underwater vehicle approaching a docking station was simulated for testing.

Main Results:

  • The proposed deep learning framework achieved higher accuracy than the iterative least squares algorithm.
  • Simulation results validated the effectiveness of the data-driven approach.
  • The framework demonstrated robustness in a realistic underwater tracking scenario.

Conclusions:

  • Deep learning offers a promising alternative to traditional methods for bearings-only target tracking.
  • The developed framework provides enhanced accuracy and efficiency for localization tasks.
  • This approach has broad applicability in fields requiring passive sensor-based tracking.