Multi-Layer Perceptron Decomposition Architecture for Mobile IoT Indoor Positioning

Abstract

We develop a Multi-Layer Perceptron (MLP) Decomposition architecture for mobile Internet Things (IoT) indoor positioning. We demonstrate the performance of our architecture on an indoor system that utilizes ultra-wideband (UWB) positioning. Our architecture outperforms the following benchmark processing techniques on the same data: MLP, Linear Regression, Ridge Regression, Support Vector Regression, and the Least Squares Method for indoor positioning. The results show that our architecture can significantly advance the positioning accuracy of indoor positioning systems and enable indoor applications such as navigation, proximity marketing, asset tracking, collision avoidance, and social distancing.