基于生物地理优化和改进的超级多维缩放的集群网络和合作本地化,用于多个无人飞行器的超级多维缩放
Shuhao Zhang1, Huimin Zhang1, Ying Zhan1
1College of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China.
Sensors (Basel, Switzerland)
|May 14, 2025
概括
本研究介绍了一种用于无人机 (UAV) 的新型合作本地化算法,该算法可优化能源并降低计算负载. 该方法提高了物联网 (IoT) 应用中的多无人机系统的本地化准确性.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 无线传感器网络 无线传感器网络
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
背景情况:
- 无人机的合作本地化对于物联网 (IoT) 任务至关重要.
- 高能耗和计算复杂性源于多无人机系统中频繁的数据交换.
- 现有的方法在动态无人机网络中努力平衡效率和准确性.
研究的目的:
- 为无人机提出一种节能且在计算上不那么复杂的合作本地化算法.
- 在多无人机合作本地化中提高相对和全球定位的准确性.
- 在资源管理和精度方面解决传统方法的局限性.
主要方法:
- 整合生物地理优化 (BO) 以基于剩余能量和密度进行能源意识的集群头部选择.
- 适应性采样改进的尼斯特罗姆超多维缩放 (ASNSMS) 的应用,以实现高效的内核矩阵近似.
- 利用Procrustes分析和最小方程用于坐标融合和转换到统一的全球系统.
主要成果:
- 与传统的多UAV本地化方法相比,计算复杂性的显著降低.
- 通过优化集群网络,大幅减少能源消耗.
- 证明了无人机相对和全球定位的定位精度的提升.
结论:
- 拟议的BOCN-ASNSMS算法有效地减轻了能源消耗和计算开销.
- 该方法通过改进相似度矩阵近似度和坐标融合来实现无人机的高精度全球定位.
- 这种方法为复杂的多UAV物联网场景中高效准确的合作本地化提供了有希望的解决方案.
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