A Generalizable Environment Sensing and Localization Framework

Modern wireless sensing systems increasingly adopt frameworks integrating variational inference (VI) and deep learning (DL), typically based on deep variational auto-encoder (VAE) structures. These architectures disentangle complex environmental semantics from distance-dependent signal features and map raw measurements to low-dimensional latent spaces, where position and environment latent variables are defined under Gaussian assumptions. Such integrated schemes enable simultaneous distance estimation and environment identification, yielding significant performance gains in nonline-of-sight (NLOS) detection and ranging error mitigation over conventional model-driven methods. By combining statistical theory with deep representation learning, they resolve key issues in high-dimensional data processing and latent feature modeling, thus forming a unified, scalable paradigm for advanced wireless localization and sensing research.