Deep Generative Model for Simultaneous Range Error Mitigation and Environment Identification

• URL: http://arxiv.org/abs/2305.18206v1
• Date: Tue, 23 May 2023 10:16:22 GMT
• Title: Deep Generative Model for Simultaneous Range Error Mitigation and Environment Identification
• Authors: Yuxiao Li, Santiago Mazuelas, Yuan Shen
• Abstract summary: This paper proposes a deep generative model (DGM) for simultaneous range error mitigation and environment identification. 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.
• Score: 29.827191184889898
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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