在水下监视中使用主动声纳的路线优化.
Mehmet Gokhan Metin1, Mumtaz Karatas2, Serol Bulkan1
1Department of Industrial Engineering, Marmara University, Istanbul 34854, Türkiye.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
这项研究优化了移动多态声纳网络 (MSN) 的水下监控. 一个殖民地优化 (ACO) 启发式有效地找到最佳的源路线以最大限度地覆盖区域,优于传统方法.
科学领域:
- 海洋学 海洋学 海洋学
- 声学 声学 在声学方面
- 机器人技术 机器人技术 机器人技术
背景情况:
- 多静态声纳网络 (MSN) 通过使用空间分布的源和接收器提供先进的水下监控,与传统的单静态系统不同.
- 优化移动声纳源的路径对于最大限度地提高MSN在水下环境中的有效性至关重要.
研究的目的:
- 确定移动多态主动声纳源的最佳路线,以最大限度地覆盖区域.
- 开发和评估用于解决MSN中的移动源路由问题的计算方法.
主要方法:
- 开发了一种混合整数线性程序 (MILP) 公式,以在六边形网格中找到最佳的单源路线.
- 一个殖民地优化 (ACO) 启发式被提出作为一个有效的方法来解决大规模的MSN路由问题.
主要成果:
- 该研究提出了一种新的MILP配方,用于MSN中最佳的源路由.
- 拟议的ACO启发式在解决复杂的路由实例方面表现出效率.
- 数字实验验证了MILP和ACO方法的性能.
结论:
- 开发的MILP和ACO方法为优化多态声纳网络中的移动源路线提供了有效的解决方案.
- 这些方法通过最大限度地覆盖区域来增强水下监视能力.
- 该研究有助于自主水下系统和监控技术的发展.
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