Indoor location-based services rely on the availability of sufficiently accurate positioning in indoor spaces. A popular approach to positioning relies on so-called radio maps that contain pairs of a vector of Wi-Fi signal strength indicator values (RSSIs), called a fingerprint, and a location label, called a reference point (RP), in which the fingerprint was observed. The positioning accuracy depends on the quality of the radio maps and their fingerprints. Radio maps are often sparse, with many pairs containing vectors missing many RSSIs as well as RPs. Aiming to improve positioning accuracy, we present a complete set of techniques to impute such missing values in radio maps. We differentiate two types of missing RSSIs: missing not at random (MNAR) and missing at random (MAR). Specifically, we design a framework encompassing a missing RSSI differentiator followed by a data imputer for missing values. The differentiator identifies MARs and MNARs via clustering-based fingerprint analysis. Missing RSSIs and RPs are then imputed jointly by means of a novel encoder-decoder architecture that leverages temporal dependencies in data collection as well as correlations among fingerprints and RPs. A time-lag mechanism is used to consider the aging of data, and a sparsity-friendly attention mechanism is used to focus attention score calculation on observed data. Extensive experiments with real data from two buildings show that our proposal outperforms the alternatives with significant advantages in terms of imputation accuracy and indoor positioning accuracy.

翻译：室内位置服务依赖于室内空间中足够精确的定位可用性。一种流行的定位方法依赖于所谓的无线电地图，其中包含一对数据：Wi-Fi信号强度指示值（RSSI）向量（称为指纹）和观测到该指纹的位置标签（称为参考点）。定位精度取决于无线电地图及其指纹的质量。无线电地图通常存在稀疏性问题，许多数据对中的向量缺失大量RSSI值以及参考点。为提高定位精度，我们提出了一套完整的技术来填补无线电地图中的这些缺失值。我们区分了两种缺失RSSI类型：非随机缺失和随机缺失。具体而言，我们设计了一个框架，包含缺失RSSI区分器与基于数据填补的缺失值处理模块。该区分器通过基于聚类的指纹分析识别随机缺失与非随机缺失。随后，通过一种新型编码器-解码器架构联合填补缺失的RSSI值和参考点，该架构利用数据采集中的时间依赖性以及指纹与参考点之间的相关性。采用时滞机制考虑数据老化，并设计稀疏性友好型注意力机制将注意力分数计算聚焦于观测数据。基于两栋建筑真实数据的广泛实验表明，本方案在填补精度和室内定位精度方面均显著优于现有方法。