基于超宽带技术的双扩展卡尔曼过算法,用于减弱复杂的室内环境中的非视线错误,基于超宽带技术
Sheng Xu1, Qianyun Liu2, Min Lin3
1School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
一种新的双扩展卡尔曼波器 (DEKF) 算法通过使用超宽带传感器的到达时间 (TOA) 数据来缓解非视线 (NLOS) 错误来提高室内目标跟踪准确性.
科学领域:
- 机器人和自动化机器人与自动化
- 信号处理 信号处理
- 导航系统 导航系统
背景情况:
- 室内目标跟踪受到非视线 (NLOS) 错误和测量不准确的挑战.
- 现有的算法在有限的视线 (LOS) 或不可预测的NLOS噪音的环境中扎着性能下降.
研究的目的:
- 提出一个双扩展卡尔曼波器 (DEKF) 算法,用于强大的室内目标跟踪.
- 在具有显著NLOS条件的复杂环境中提高准确性和可靠性.
主要方法:
- 一个级联的DEKF结构,结合了经典的卡尔曼波器 (KF) 和扩展的卡尔曼波器 (EKF).
- 使用固定超宽带 (UWB) 传感器的到达时间 (TOA) 测量.
- 预测剩余误差并调整KF共变率,使用KF估计的距离作为EKF输入,以实现更平稳的观测.
主要成果:
- DEKF算法甚至在最小或没有LOSTOA测量时也显示出高精度.
- 在持续的NLOS噪音下长时间保持跟踪精度.
- 在恒定速度 (CV) 和恒定加速度 (CA) 运动模型中有效执行.
结论:
- 拟议的DEKF算法在混合LOS/NLOS环境中显著优于最先进的方法.
- 尽管具有挑战性的环境因素,但为精确的室内定位提供了强大的解决方案.
- DEKF为室内目标追踪应用提供了可靠和准确的方法.
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