Indoor positioning system has been an essential work to substitute the Global Positioning System (GPS). GPS utilizing Global Navigation Satellite Systems (GNSS) cannot provide an accurate positioning in the indoor due to the multipath effect and shadow fading. Fingerprinting method with Wi-Fi technology is a promising system to solve this issue. However, there are several problems with the fingerprinting method. The fingerprinting database collected has different sample sizes where the previous researcher does not indicate any standard for the sample size to be used. In this paper, the effect of the sample sizes in fingerprinting database for Wi-Fi technology has been discussed deeply. The statistical analyzation for different sample sizes has been analyzed. Furthermore, two methods which are K- Nearest Neighbor (KNN) and Deep Neural Network (DNN) are being used to examine the effect of the sample sizes in term of accuracy and distance error. The discussion in this paper will contribute to the better sample size selection depending on the method taken by the user. The result shows that sample sizes are an important metrics in developing the indoor positioning system as it effects the result of the location estimation.

中文翻译：

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Wi-Fi系统指纹数据库中样本大小的影响

室内定位系统已经成为替代全球定位系统（GPS）的一项必不可少的工作。由于多径效应和阴影衰落，使用全球导航卫星系统 (GNSS) 的 GPS 无法在室内提供准确定位。采用Wi-Fi技术的指纹识别方法是解决这个问题的一个很有前途的系统。然而，指纹识别方法存在几个问题。收集的指纹数据库具有不同的样本量，以前的研究人员没有说明要使用的样本量的任何标准。本文深入讨论了指纹数据库中样本大小对Wi-Fi技术的影响。对不同样本量的统计分析进行了分析。此外，K-最近邻 (KNN) 和深度神经网络 (DNN) 这两种方法被用于检查样本大小在精度和距离误差方面的影响。本文中的讨论将有助于根据用户采用的方法更好地选择样本量。结果表明，样本大小是开发室内定位系统的重要指标，因为它会影响位置估计的结果。