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Passive Location Resource Scheduling Based on an Improved Genetic Algorithm.

Jianjun Jiang1, Jing Zhang2, Lijia Zhang3

  • 1National Digital Switching System Engineering and Technological Research Center (NDSC), Zhengzhou 450000, China. jiang84715601@outlook.com.

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Summary
This summary is machine-generated.

This study introduces a new scheduling method, MMIGAS, for passive location systems. It significantly improves efficiency and maintains high location precision compared to manual methods.

Keywords:
NP-hardangle-of-arrivalgenetic algorithmpassive locationscheduling

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Area of Science:

  • Engineering
  • Computer Science

Background:

  • Modern communication demands advanced passive location technology.
  • Current manual scheduling methods are inefficient for intensive, distributed scenarios.

Purpose of the Study:

  • To develop an efficient scheduling method for passive location systems.
  • To address the limitations of manual scheduling in complex scenarios.

Main Methods:

  • Developed a centralized combinatorial optimization model with multiple objectives and constraints.
  • Proposed an improved genetic algorithm (MMIGAS) with enhanced convergence and global optimization.
  • Incorporated constraint-proof initialization, a penalty function, and modified genetic operators.

Main Results:

  • MMIGAS demonstrates good convergence and steady time complexity.
  • The method achieves satisfactory location accuracy.
  • Simulations show improved efficiency over manual scheduling while maintaining precision.

Conclusions:

  • MMIGAS offers an effective solution for optimizing passive location systems.
  • The proposed algorithm enhances task completion efficiency and location accuracy.
  • This approach is suitable for mission-intensive and widely-distributed scenarios.