这篇文章研究了在磁追踪中最常用的三种定位方法，并在多个磁铁追踪的情形下进行了仿真测试，最后得出TRR算法的可靠性最高的结论。可以给研究磁定位的研究人员以参考。
用于多磁目标追踪的在线算法的比较
Comparison of online algorithms for the tracking of multiple magnetic targets in a myokinetic control interface [1]
Paper Link暂时还没有，来自 ICRA 2020 会议网站
Authors: J. Montero, et al.
2020, IEEE International Conference on Robotics and Automation (ICRA)
0. 摘要 Abstract
磁跟踪算法能被用来决定磁标记或者设备的位置和朝向。这些技术对于生物医疗应用来说非常有意思，比如远程操控手术机器人或者上肢体假肢的控制。用于磁追踪的不同算法的表现在过去被比较。但是，在大多数情况下，那些算法被要求来追踪一个单个磁铁。这儿我们调查三个定位算法在追踪高达9个磁铁的表现：两个基于优化的（LM和TRR）和一个基于递归的。算法的追踪精度和它们的计算时间在仿真中被调查。当追踪高达6个磁铁的时候，三个算法的精度，是相似的，导致估计误差在0.06到2.26mm之间变化在10054100的工作空间范围内，在最高的采样频率。在所有情况下，对于UKF来说低于300ms，对于LM/TRR来说低于45ms的计算时间是可获得的。TRR展示了整体上最好的追踪表现。
Magnetic tracking algorithms can be used to determine the position and orientation of magnetic markers or devices. These techniques are particularly interesting for biomedical applications, such as teleoperated surgical robots or the control of upper limb prostheses. The performance of different algorithms used for magnetic tracking was compared in the past. However, in most cases, those algorithms were required to track a single magnet. Here we investigated the performance of three localization algorithms in tracking up to 9 magnets: two optimization-based and one recursion-based. The tracking accuracy and their computation time were investigated through simulations. The accuracy of three algorithms, when tracking up to 6 magnets, was similar,leading to estimation errors varying from 0.06mm to 2.26mm within a 10054100mm workspace, at the highest sampling frequency. In all cases, computation times under 300ms for the UKF and 45ms for the LM/TRR were obtained. The TRR showed the best tracking performance overall.
2. B. 仿真设置 Simulation Setup
不同数量的磁铁（1个/2个/4个/6个/9个），工作区间100mm54mm100，四面传感器，每一面传感器都是7*4阵列，共112个传感器。
3. 结果 Results
在动态测试中，只有一个磁铁是模拟运动的，剩余的不动。
[1]: J. Montero et al. “Comparison of online algorithms for the tracking of multiple magnetic targets in a myokinetic control interface.” IEEE International Conference on Robotics and Automation (2020).