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基于人工子优化算法和BP神经网络的UWB室内定位.

Chaochuan Jia1,2, Can Tao1, Ting Yang3

  • 1College of Electronics and Information Engineering, West Anhui University, Lu'an 237000, China.

Biomimetics (Basel, Switzerland)
|June 25, 2025
PubMed
概括
此摘要是机器生成的。

这项研究介绍了ARO-BP,这是一种混合算法,可以增强超宽带室内定位. 它通过优化神经网络来提高准确性,克服了传统方法的局限性,以获得更好的定位.

关键词:
在BP神经网络中,神经网络在UWB中,UWB是UWB.人工子优化算法的人工子优化算法视线直线 - 视线直线的视线.没有视线的视线.

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科学领域:

  • 机器人和自动化 机器人和自动化
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 传统的反向传播神经网络 (BPNNs) 在超宽带 (UWB) 室内定位中与局部最小值作斗争,阻碍了全球优化.
  • 由于信号障碍和环境复杂性,实现精确的室内定位仍然是一个挑战.

研究的目的:

  • 开发一种新的混合算法,即人工子优化逆向传播神经网络 (ARO-BP),用于增强UWB室内定位.
  • 通过优化 BPNN 参数来提高室内定位模型的收性和准确性.

主要方法:

  • 将人工子优化 (ARO) 算法与BPNN集成,以优化初始权重和值.
  • 使用四个基站设置在视线 (LOS) 和非视线 (NLOS) 环境中的实验验证.
  • 在静态和动态运动场景中对标准BPNNs进行性能评估.

主要成果:

  • 在LOS条件下,ARO-BP实现了6.29厘米的局部化误差,比标准BPNN有49.48%的改善.
  • 在NLOS场景中,ARO-BP将误差降低到9.86厘米,与基线相比提高了46.96%.
  • 该算法在动态运动跟踪中表现出卓越的稳定性和准确性.

结论:

  • 拟议的ARO-BP算法有效地克服了UWB室内定位中的局部最小问题.
  • 对于复杂的室内环境,ARO-BP在精度和强度方面提供了显著的改进.
  • 这些发现支持ARO-BP在实际的室内定位应用中的潜力.