Fingerprinting based indoor positioning system is gaining more research interest under the umbrella of location-based services. However, existing works have certain limitations in addressing issues such as noisy measurements, high computational complexity, and poor generalization ability. In this work, a random vector functional link network based approach is introduced to address these issues. In the proposed system, a subset of informative features from many randomized noisy features is selected to both reduce the computational complexity and boost the generalization ability. Moreover, the feature selector and predictor are jointly learned iteratively in a single framework based on an augmented Lagrangian method. The proposed system is appealing as it can be naturally fit into parallel or distributed computing environment. Extensive real-world indoor localization experiments are conducted on users with smartphone devices and results demonstrate the superiority of the proposed method over the existing approaches.

中文翻译：

---

使用随机矢量功能链接网络的基于接收信号强度的室内定位

在基于位置的服务的保护下，基于指纹的室内定位系统正在获得越来越多的研究兴趣。但是，现有的工作在解决诸如噪声测量，高计算复杂度和较差的泛化能力等问题上有一定的局限性。在这项工作中，引入了一种基于随机矢量功能链接网络的方法来解决这些问题。在所提出的系统中，从许多随机噪声特征中选择了信息特征的子集，以降低计算复杂度并提高泛化能力。此外，基于增强的拉格朗日方法，可以在单个框架中迭代地共同学习特征选择器和预测器。所提出的系统很有吸引力，因为它可以自然地适应并行或分布式计算环境。