Kernel Methods for Accurate UWB-Based Ranging With Reduced Complexity
Kernel Methods for Accurate UWB-Based Ranging With Reduced Complexity
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DOI：
10.1109/twc.2015.2496584
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发表时间：
2016-03-01
影响因子：
10.4
通讯作者：
Stenumgaard, Peter
中科院分区：
文献类型：
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作者：
Savic, Vladimir;Larsson, Erik G.;Stenumgaard, Peter
在多路径环境中精确和稳健的定位可以实现许多应用，例如搜索和救援以及资产跟踪。针对这个问题，超宽带（UWB）技术可以提供最精确的距离估计，这是基于距离的定位所需要的。然而，UWB仍然面临着非视距（NLOS）测量的问题，其中基于到达时间（TOA）的距离估计通常会有正偏。有许多技术来解决这个问题，主要是基于NLOS识别和NLOS误差抑制算法。然而，这些技术没有利用UWB信道脉冲响应中的所有可用信息。基于核的机器学习方法，如高斯过程回归（GPR），能够利用所有信息，但它们在原始形式下可能过于复杂。在本文中，我们提出了新的测距方法的基础上，核主成分分析（kPCA），其中所选择的信道参数被投影到一个非线性正交高维空间，这些投影的子集，然后被用作测距的输入。我们评估所提出的方法使用真实的UWB测量在地下室隧道，并发现所提出的方法之一是能够超越国家的最先进的，即使很少的训练样本。
Accurate and robust positioning in multipath environments can enable many applications, such as search-andrescue and asset tracking. For this problem, ultra-wideband (UWB) technology can provide the most accurate range estimates, which are required for range-based positioning. However, UWB still faces a problem with non-line-of-sight (NLOS) measurements, in which the range estimates based on time-of-arrival (TOA) will typically be positively biased. There are many techniques that address this problem, mainly based on NLOS identification and NLOS error mitigation algorithms. However, these techniques do not exploit all available information in the UWB channel impulse response. Kernel-based machine learning methods, such as Gaussian process regression (GPR), are able to make use of all information, but they may be too complex in their original form. In this paper, we propose novel ranging methods based on kernel principal component analysis (kPCA), in which the selected channel parameters are projected onto a nonlinear orthogonal high-dimensional space, and a subset of these projections is then used as an input for ranging. We evaluate the proposed methods using real UWB measurements obtained in a basement tunnel, and found that one of the proposed methods is able to outperform state-of-the-art, even if little training samples are available.