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Related Concept Videos

Orthogonal Trajectories01:26

Orthogonal Trajectories

Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...
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Absolute Motion Analysis- General Plane Motion

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Related Experiment Video

Updated: May 14, 2026

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

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Published on: May 1, 2018

Multi-UAV-Borne Surveillance Radar Trajectory Planning Method Based on Imitation Learning.

Xuchao Gao1, Mingqiang Li1, Kai Guan1

  • 1Information Science Academy, China Electronics Technology Group Corporation, Beijing 100086, China.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
Summary

This study introduces an imitation-learning trajectory planning method for multi-radar systems, enhancing real-time performance and coverage. The novel approach significantly boosts decision efficiency in complex sensing environments.

Keywords:
coverage modelingimitation learningmulti-radarsignal interference countermeasurestrajectory planning

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Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
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Last Updated: May 14, 2026

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06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Area of Science:

  • Robotics and Control Systems
  • Artificial Intelligence
  • Sensor Networks

Background:

  • Traditional multi-radar trajectory planning faces challenges with computational complexity and real-time performance in complex scenarios.
  • Effective trajectory planning is crucial for optimizing multi-platform sensing capabilities.

Purpose of the Study:

  • To develop an efficient and real-time trajectory planning method for multi-radar systems using imitation learning.
  • To improve the coverage rate and decision-making speed in multi-platform sensing.

Main Methods:

  • Proposed an imitation-learning-based trajectory planning method for multi-radar systems.
  • Developed a trajectory policy neural network architecture incorporating multi-semantic information.
  • Constructed training data with coverage rate as the optimization objective.
  • Utilized an imitation-learning algorithm with an auxiliary target for neural network training.

Main Results:

  • Achieved an average coverage rate of 93.95% in simulations.
  • Improved single-step decision efficiency by a factor of 6.7 compared to heuristic-based methods.
  • Demonstrated superior real-time performance and computational efficiency.

Conclusions:

  • The proposed imitation-learning method effectively addresses the limitations of traditional trajectory planning in multi-radar systems.
  • The method offers significant improvements in coverage rate and decision efficiency for complex multi-platform sensing.
  • This approach provides a viable solution for real-time trajectory optimization in dynamic environments.