Exploring the fusion of multi-power levels in received signal strength indicator-to-image transformation for accurate indoor positioning with deep learning

In this study, we present an indoor localization system leveraging Zigbee-based hardware for real-time positioning. We conduct in-house measurements in a controlled environment to generate a robust dataset for the system. A six-step methodology is employed, encompassing received signal strength indicator (RSSI)-to-image transformation, feature extraction using a pre-trained convolutional neural network (CNN) model, dimensionality reduction, clustering, and classification. By converting RSSI values into images, we capture spatial patterns more effectively. A multi-power level fusion strategy combines RSSI data from three distinct power levels into RGB images, improving localization accuracy and robustness against interference. K-means clustering segments the environment into distinct zones, streamlining classification. Experiments demonstrate the significant contributions of power level fusion, clustering, and classification, with the multi-power level fusion yielding improved performance compared to single power levels. Our approach, based on a custom CNN architecture, offers enhanced precision and efficiency for indoor localization tasks, providing valuable insights into the integration of advanced signal processing and deep learning.