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用RSS测量进行源定位的修改差异演变

Yunjie Tao¹, Lincan Li¹, Shengming Chang¹

¹School of Cyber Science and Engineering, Ningbo University of Technology, Ningbo 315211, China.

Sensors (Basel, Switzerland)

|June 27, 2025

概括

这项研究通过基于对立的学习 (OBL) 来增强差异演化 (DE) 对于无线传感器网络本地化. 改进的算法实现了更快的融合和更高的定位精度,在具有挑战性的环境中超过现有方法.

科学领域:

计算机科学计算机科学
电气工程电气工程
网络工程网络工程

背景情况:

无线传感器网络 (WSNs) 面临着局部化挑战,原因是使用接收信号强度 (RSS) 的非凸和非线性最大概率估计问题.
差分演变 (DE) 对于多式联运成本函数是有效的,但受到缓慢的收和局部最佳的困扰.
现有的本地化技术,如半定义编程和线性最小方程,在复杂的WSN场景中存在局限性.

研究的目的:

为改进WSN本地化提出一个全新的增强差异演化 (DE) 算法,与基于对立的学习 (OBL) 集成.
为了解决传统DE的局限性,特别是低于最佳的融合率和对局部最佳的敏感性.
用RSS测量来提高WSN本地化中的定位精度和融合速度.

主要方法:

将基于对立的学习 (OBL) 原则纳入差异进化 (DE) 框架.
引入适应性缩放因子,以平衡全球勘探和当地开采.
开发一个罚款增值成本函数,以利用边界信息并消除明确的约束处理.

主要成果:

与最先进的方法相比,提议的增强DE算法在融合速度和定位精度方面取得了显著的改进.
对比评估显示优于半确定的编程,线性最小正方形和模拟化.
实验结果验证了算法的稳定性和有效性,在各种噪声条件和网络配置下,特别是在稀疏的节点下.

结论:

拟议的基于对立的学习增强差异演化 (OBL-DE) 为WSN本地化提供了一种优越的方法.
适应性扩展因子和惩罚性增加成本函数有效地提高了复杂和不确定的环境中的性能.
这种增强的DE方法为无线传感器网络中基于接收信号强度的定位提供了强大而准确的解决方案.

关键词:

差异进化 (DE) 是指差异进化的过程.基于对立的学习 (OBL)收到的信号强度 (RSS)源的本地化源的本地化无线传感器网络 (WSN) 是指无线传感器网络.

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