注意机制和LSTM网络用于基于指纹的室内定位系统
Zhen Wu1, Peng Hu1, Shuangyue Liu1
1Department of Mobile Communications and Terminal Research, China Telecom Research Institute, Guangzhou 510000, China.
Sensors (Basel, Switzerland)
|March 13, 2024
概括
这项研究引入了一种新的室内定位方法,使用自我注意力和长期短期记忆 (LSTM) 来提高准确性. 拟议的技术在动态环境中显著提高了定位性能,超过了现有的方法.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 机器人技术 机器人技术 机器人技术
背景情况:
- 对于精确的室内定位服务的需求日益增加.
- 基于指纹的室内定位很受欢迎,但受到多路径减弱和环境变化的影响.
- 需要强大而准确的室内定位解决方案.
研究的目的:
- 提出一种先进的室内定位方法,利用自我注意力和长短期记忆 (LSTM).
- 为了提高定位准确性和对环境变化的稳定性.
- 在2D和3D轨迹场景中评估方法的性能.
主要方法:
- 开发了一种室内本地化方法,将自我注意机制与LSTM网络结合起来.
- 实施了基于指纹的本地化策略.
- 评估了2D和3D移动轨迹数据集上的方法.
主要成果:
- 在2D场景中实现了1.76m的平均定位误差.
- 在3D场景中实现了2.83m的平均定位误差.
- 它的性能比最先进的方法高42.67% (2D) 和31.64% (3D).
结论:
- 提出的自我注意和基于LSTM的指纹采集方法为室内定位提供了卓越的准确性和稳定性.
- 该方法有效地解决了由环境动态引起的性能退化问题.
- 与现有的室内定位技术相比,显示了显著的改进.
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