精确定位方法 结合优化混合神经网络用于地磁定位与多功能死计算
Suqing Yan1,2, Baihui Luo2, Xiyan Sun3
1Guangxi Key Laboratory of Precision Navigation Technology and Application, Guilin University of Electronic Technology, Guilin 541004, China.
Sensors (Basel, Switzerland)
|March 17, 2025
概括
本研究介绍了一种使用粒子群优化 (PSO) 的新型聚变定位算法,以增强地磁定位和标题估计. 该方法提高了基于位置的服务的准确性和稳定性.
科学领域:
- 地理学工程 工程地质学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 基于位置的服务 (LBS) 提供了显著的经济和社会优势.
- 地磁学提供了一个无处不在的,低成本的,可访问的本地化资源.
- 现有的地磁定位技术面临着位置模两可的挑战.
研究的目的:
- 开发一个强大而准确的融合本地化算法.
- 为了克服传统地磁定位方法的局限性.
- 为了提高航向估计,以改善死亡计算.
主要方法:
- 为地磁定位开发了一个五维混合LSTM (5DHLSTM) 神经网络,使用粒子群优化 (PSO) 优化参数.
- 八维BiLSTM (8DBiLSTM) 算法用于在行人死亡计算 (PDR) 中准确的方向估计.
- 通过将地磁定位与使用扩展卡尔曼波器 (EKF) 的改进的PDR (IPDR) 集成,实现了融合定位.
主要成果:
- 拟议的PSO-5DHLSTM-IPDR方法在实验验证中显示出更好的定位精度.
- 该算法在不同的场景中表现出良好的稳定性和灵活性.
- 通过8DBiLSTM算法实现了更高的定位精度,有助于整体定位性能.
结论:
- 开发的融合定位算法有效地解决了地磁定位中的位置模糊性.
- 将PSO优化的5DHLSTM和8DBiLSTM与基于EKF的IPDR集成,为LBS提供了一个优越的解决方案.
- 该方法显示了需要精确可靠的定位的实际应用的巨大潜力.
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