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.

翻译：接收波形包含丰富的测距信息与环境语义信息，然而在多径和非视距条件下其全部潜力难以被充分利用。本文提出一种深度生成模型（DGM），用于同时实现测距误差抑制与环境识别。具体而言，我们构建了一个贝叶斯模型来描述接收波形的生成过程，该过程由测距相关特征和环境语义的潜变量共同组成。基于该深度生成模型，同时进行的测距误差抑制与环境识别被解释为一种推理问题，并通过独特的端到端学习方案实现。在通用超宽带数据集上的综合实验表明，该方法在测距误差抑制、对不同环境的可扩展性以及同时环境识别的新颖能力方面均表现出优越性能。