Now showing items 1-6 of 6
Variational Bayesian Framework for Advanced Image Generation with Domain-Related Variables
Deep generative models (DGMs) and their conditional counterparts provide a powerful ability for general-purpose generative modeling of data distributions. However, it remains challenging for existing methods to address ...
A Deep Learning Approach for Generating Soft Range Information from RF Data
Radio frequency (RF)-based techniques are widely adopted for indoor localization despite the challenges in extracting sufficient information from measurements. Soft range information (SRI) offers a promising alternative ...
A Semi-Supervised Learning Approach for Ranging Error Mitigation Based on UWB Waveform
Localization systems based on ultra-wide band (UWB) measurements can have unsatisfactory performance in harsh environments due to the presence of non-line-of-sight (NLOS) errors. Learning-based methods for error ...
Deep GEM-based network for weakly supervised UWB ranging error mitigation
Ultra-wideband (UWB)-based techniques, while becoming mainstream approaches for high-accurate positioning, tend to be challenged by ranging bias in harsh environments. The emerging learning-based methods for error ...
Deep Generative Model for Simultaneous Range Error Mitigation and 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 ...
Spatiotemporal information coupling in network navigation
Network navigation, encompassing both spatial and temporal cooperation to locate mobile agents, is a key enabler for numerous emerging location-based applications. In such cooperative networks, the positional information ...