A review of deep generative model techniques for Bayesian inverse problems in high-rate sensor data, covering structured noise, companding, model-based scores, and acceleration methods.
2022 Reframing Fast-Chirp FMCW Transceivers for Future Automotive Radar: The pathway to higher resolution.IEEE Solid-State Circuits Magazine 14, 44–55
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Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging
A review of deep generative model techniques for Bayesian inverse problems in high-rate sensor data, covering structured noise, companding, model-based scores, and acceleration methods.