Heterogeneous sensing based indoor localization leveraging Bayesian prior estimation

Indoor wireless localization is critical for enabling a wide range of mobile and IoT applications, such as elder monitoring, robot navigation, and augmented reality/virtual reality. Current wireless localization techniques rely on homogeneous sensing, utilizing single-modality signals like Bluetooth, WiFi, or mmWave, which are susceptible to in-channel interference, multipath distortions, and environmental variability (e.g., device position and furniture placement changes). In this paper, we design a heterogeneous sensing system that combines wireless signals of multiple modalities to enhance indoor localization accuracy. Through building a Bayesian-based framework, we statistically integrate location fingerprints from various sensing modalities to address the nonlinearities introduced by spatial and temporal fluctuations. Our approach is generalizable and can be applied to existing fingerprinting localization methods based on machine learning algorithms, such as K-nearest neighbors (KNN), support vector machines (SVM), and deep learning models, significantly enhancing the localization performance and robustness. Extensive real-world experiments demonstrate that our system reduces the average localization errors from 2.1 m to 1.23 m, even in the presence of complex environmental dynamics.