Wi-Fi fingerprint systems provide cost-effective and reliable solution for indoor positioning. However, such systems incur high calibration cost in the training phase and high searching overhead in the positioning phase. Moreover, huge storage requirement for the radio map of a large-scale fingerprint system is another major issue. Several solutions based on crowd-sourcing or machine learning technique have been proposed in literature to reduce the calibration overhead. On the other hand, various clustering methods have been proposed over the past decade to reduce the searching overhead. However, none of the existing systems has addressed the issue of high storage requirement for the fingerprint database constructed in the training phase. Moreover, presence of outlier in the received signal strength (RSS) measurements severely impacts the positioning accuracy of such systems. Thus, this paper proposes an efficient clustering strategy for fingerprint based positioning systems to reduce the storage overhead and searching overhead incurred by such systems and also proposes a robust outlier mitigation technique to improve their positioning accuracy. The performances of our proposed positioning system are evaluated and compared with five existing fingerprint techniques in both the simulation test bed as well as real indoor environment via extensive experimentation. The experimental results demonstrate that our proposed system can not only reduce the storage overhead and searching overhead but also improve the positioning accuracy compared to the other existing techniques.

Wi-Fi指纹系统为室内定位提供了经济高效且可靠的解决方案。然而，这样的系统在训练阶段会产生高校准成本，在定位阶段会产生高搜索开销。此外，大规模指纹系统的无线电地图的巨大存储需求是另一个主要问题。文献中已经提出了几种基于众包或机器学习技术的解决方案，以减少校准开销。另一方面，在过去十年中已经提出了各种聚类方法来减少搜索开销。然而，现有系统都没有解决训练阶段构建的指纹数据库存储要求高的问题。此外，接收信号强度中存在异常值( RSS ) 测量严重影响此类系统的定位精度。因此，本文为基于指纹的定位系统提出了一种有效的聚类策略，以减少此类系统产生的存储开销和搜索开销，并提出了一种稳健的 异常值缓解技术以提高其定位精度。通过广泛的实验，我们对我们提出的定位系统的性能进行了评估，并与模拟试验台和真实室内环境中的五种现有指纹技术进行了比较。实验结果表明，与其他现有技术相比，我们提出的系统不仅可以减少存储开销和搜索开销，还可以提高定位精度。