Consider indoor positioning systems (IPS) in production halls where objects equipped with sensors send their current position. Beside its large volume, the analyzation of the resulting raw data is challenging due to the susceptibility towards noise. Reasons are accuracy issues and undesired awakenings of sensors that occur due to the dynamics of logistic processes (e.g.~vibrations of passing forklifts). We propose a tailor-made statistical procedure for these challenges and combine visual analytics with movement detection. Contrary to common stay-point algorithms, we do not only distinguish between stops and moves, but also consider undesired awakenings. This leads to a more detailed interpretation scheme offering usages for online (e.g.~monitoring of orders) and offline applications (e.g.~detection of problematic areas). The approach does not require other information than the raw IPS output and enables an ad-hoc analysis. We underline our findings in an extensive case study with real IPS data of our industry partner.

翻译：在生产车间中，物体搭载传感器发送实时位置时，室内定位系统（IPS）面临挑战。除数据量大外，原始数据的分析因易受噪声干扰而困难重重。噪声源于精度不足及物流过程动态性（如叉车驶过引起的振动）导致的传感器非预期唤醒。针对这些问题，我们提出了一种定制化统计算法，将可视化分析与运动检测相结合。与常规停留点算法不同，我们不仅区分静止与运动状态，还特别处理非预期唤醒现象。由此构建的更精细化解译方案，可同时支持在线应用（如订单监控）与离线应用（如问题区域检测）。该方法仅需原始IPS输出数据即可实现即时分析。我们通过工业合作伙伴的真实IPS数据案例研究，充分验证了上述结论。