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A comparison of Bayesian localization methods in the presence of outliers

机译：存在异常值时的贝叶斯定位方法比较

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Localization of a user in a wireless network is challenging in the presence of malfunctioning or malicious reference nodes, since if they are not accounted for, large localization errors can ensue. We evaluate three Bayesian methods to statistically identify outliers during localization: an exact method, an expectation maximization (EM) method proposed earlier, and a new method based on Variational Bayesian EM (VBEM). Simulation results indicate similar performance for the latter two schemes, with the VBEM algorithm able to provide a statistical description of the user location, rather than an estimate as in the simpler EM case. In contrast to previous studies, we find that there is a significant gap between the approximate methods and the exact method, the cause of which is discussed.
机译：在存在故障或恶意参考节点的情况下，用户在无线网络中的本地化具有挑战性，因为如果不考虑这些错误或错误的参考节点，则可能会出现较大的本地化错误。我们评估了三种贝叶斯方法以在定位过程中统计地识别异常值：一种精确方法，一种较早提出的期望最大化（EM）方法以及一种基于变分贝叶斯EM（VBEM）的新方法。仿真结果表明后两种方案具有相似的性能，VBEM算法能够提供用户位置的统计描述，而不是像更简单的EM情况那样提供估计。与以前的研究相比，我们发现近似方法和精确方法之间存在很大的差距，并对其原因进行了讨论。

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