Multi-Layer Perceptron Decomposition Architecture for Mobile IoT Indoor Positioning

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.