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Non-Myopic Beam Scheduling for Multiple Smart-Target Tracking in Phased Array Radar Networks.

Yuhang Hao1,2, Zengfu Wang1,2, José Niño-Mora3

  • 1School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.

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|December 17, 2024
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Summary
This summary is machine-generated.

This study introduces a new beam scheduling method for phased array radar networks to improve smart target tracking. The unscented sampling-based marginal productivity (US-MP) policy enhances tracking performance and mitigates degradation from prior methods.

Keywords:
Whittle indexbeam schedulingrestless banditstarget tracking

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Area of Science:

  • Radar Systems Engineering
  • Control Theory
  • Signal Processing

Background:

  • Phased array radar networks face performance degradation in tracking multiple smart targets due to myopic scheduling.
  • Existing methods struggle with dynamic state information and non-linear measurement functions for accurate tracking.

Purpose of the Study:

  • To enhance tracking performance in phased array radar networks by addressing limitations of myopic scheduling.
  • To develop an effective beam scheduling policy for multiple smart targets using a novel approach.

Main Methods:

  • Formulated the beam scheduling problem as a restless multi-armed bandit problem.
  • Employed unscented sampling to predict dynamic and tracking error covariance (TEC) states for marginal productivity (MP) index computation.
  • Proposed an unscented sampling-based MP (US-MP) index policy for target selection.

Main Results:

  • The proposed US-MP policy demonstrated effectiveness in simulations for practical scenarios.
  • The bandit model with a scalar Kalman filter satisfied conditions for indexability.
  • The US-MP policy is applicable to large radar networks with numerous targets.

Conclusions:

  • The US-MP policy offers a significant improvement over previous methods for multi-target tracking in phased array radar.
  • This approach provides a robust and scalable solution for dynamic radar network management.
  • Validated effectiveness in realistic scenarios, confirming its practical applicability.