Deep Generative Model for Simultaneous Range Error Mitigation and Environment Identification
This addresses the challenge of accurate localization and environment understanding in wireless systems, but it appears incremental as it builds on existing deep generative and Bayesian methods for a specific domain.
The paper tackles the problem of exploiting received waveforms for range information and environment semantics under multipath and non-line-of-sight conditions by proposing a deep generative model, achieving superior performance in range error mitigation and novel capability in simultaneous environment identification.
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.