Location awareness is vital for emerging Internet-of-Things applications and opens a new era for Localization-of-Things. This paper first reviews the classical localization techniques based on single-value metrics, such as range and angle estimates, and on fixed measurement models, such as Gaussian distributions with mean equal to the true value of the metric. Then, it presents a new localization approach based on soft information (SI) extracted from intra- and inter-node measurements, as well as from contextual data. In particular, efficient techniques for learning and fusing different kinds of SI are described. Case studies are presented for two scenarios in which sensing measurements are based on: 1) noisy features and non-line-of-sight detector outputs and 2) IEEE 802.15.4a standard. The results show that SI-based localization is highly efficient, can significantly outperform classical techniques, and provides robustness to harsh propagation conditions.

中文翻译：

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事物定位的软信息

位置感知对于新兴的物联网应用至关重要，并开启了物联网的新时代。本文首先回顾了基于单值度量（例如范围和角度估计）和固定测量模型（例如均值等于度量真实值的高斯分布）的经典定位技术。然后，它提出了一种基于从节点内和节点间测量以及上下文数据中提取的软信息 (SI) 的新定位方法。特别是，描述了用于学习和融合不同类型 SI 的有效技术。案例研究针对传感测量基于的两种情况进行了介绍：1) 噪声特征和非视线检测器输出以及 2) IEEE 802.15.4a 标准。