Deep Generative Model for Simultaneous Range Error Mitigation and
  Environment Identification
Yuxiao Li Santiago Mazuelas Yuan Shen
Received waveforms contain rich information for both range information and
environment semantics. However, its full potential is hard to exploit under
multipath and non-line-of-sight conditions. This paper proposes a deep
generative model (DGM) for simultaneous range error mitigation and environment
identification. In particular, we present a Bayesian model for the generative
process of the received waveform composed by latent variables for both
range-related features and environment semantics. The simultaneous range error
mitigation and environment identification is interpreted as an inference
problem based on the DGM, and implemented in a unique end-to-end learning
scheme. Comprehensive experiments on a general Ultra-wideband dataset
demonstrate the superior performance on range error mitigation, scalability to
different environments, and novel capability on simultaneous environment
identification.