The performance of a range-based indoor positioning system is severely degraded by non-line-of-sight (NLOS) propagation due to the offsets in range measurements (i.e., NLOS errors). It is difficult to predict or mitigate the NLOS errors since they are dependent on both the location and the environment. In this paper, we propose an accurate tracking scheme for NLOS environments by jointly estimating the target's trajectory and the NLOS errors based on the fusion of sensors that measure the motion of the target. We first formulate a maximum a posteriori (MAP) estimation problem with generic equality constraints that capture the spatial correlation of NLOS errors. A specific constraint function based on Gaussian process (GP) regression is then provided, and an iterative algorithm is proposed to solve the optimization problem. The proposed scheme is validated experimentally in an indoor positioning system with 125 MHz bandwidth using a mobile node equipped with an inertial measurement unit. It is shown that the median positioning error in an office environment is reduced by 90% to 11 cm compared to using conventional tracking algorithms.

中文翻译：

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用于在 NLOS 环境中进行精确跟踪的联合轨迹和测距偏移估计

由于距离测量中的偏移（即 NLOS 误差），非视距 (NLOS) 传播会严重降低基于距离的室内定位系统的性能。很难预测或减轻 NLOS 错误，因为它们取决于位置和环境。在本文中，我们基于测量目标运动的传感器的融合，通过联合估计目标的轨迹和 NLOS 误差，为 NLOS 环境提出了一种准确的跟踪方案。我们首先制定了一个最大后验 (MAP) 估计问题，该问题具有捕获 NLOS 误差的空间相关性的通用等式约束。然后给出基于高斯过程（GP）回归的特定约束函数，并提出迭代算法来解决优化问题。所提出的方案在具有 125 MHz 带宽的室内定位系统中使用配备有惯性测量单元的移动节点进行了实验验证。结果表明，与使用传统跟踪算法相比，办公环境中的中位定位误差降低了 90% 至 11 厘米。