Updating radio map quickly and accurately is very challenging when the crowdsourcing radio fingerprints is provided by the unprofessional volunteers in WLAN indoor positioning system. To solve the problem, we propose a sparse representation and low rank matrix recovery based radio map update method. This method uses fingerprint correlation learned by sparse representation to complete the radio map consisting of fingerprint patches. Furthermore, the low-rank and sparse prior are combined skillfully in our proposed method to handle the fingerprint missing and sparse noise. Based on our analysis and experimental results, the proposed method significantly outperforms the state-of-the-art radio map update method even when the available crowdsourcing data accounts for a low degree of the entire radio map volume.

中文翻译：

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基于稀疏表示和低秩矩阵恢复的无线地图众包更新方法用于WLAN室内定位系统

当无线局域网室内定位系统中的非专业志愿者提供众包无线电指纹时，快速准确地更新无线电地图是非常具有挑战性的。为了解决这个问题，我们提出了一种基于稀疏表示和低秩矩阵恢复的无线电地图更新方法。该方法利用通过稀疏表示学习的指纹相关性来完成由指纹补丁组成的无线电地图。此外，在我们提出的方法中巧妙地结合了低秩和稀疏先验来处理指纹丢失和稀疏噪声。根据我们的分析和实验结果，即使可用的众包数据占整个无线电地图体积的比例很低，所提出的方法也明显优于最先进的无线电地图更新方法。