This paper presents the first subject-specific head pose estimation approach using only one Frequency Modulated Continuous Wave (FMCW) radar data frame. Specifically, the proposed method incorporates a deep learning (DL) framework to estimate head pose rotation and orientation frame-by-frame by combining a Convolutional Neural Network operating on Range-Angle radar plots and a PeakConv network. The proposed method is validated with an in-house collected dataset, including annotated head movements that varied in roll, pitch, and yaw, and these were recorded in two different indoor environments. It is shown that the proposed model can estimate head poses with a relatively small error of approximately 6.7-14.4 degrees for all rotational axes and is capable of generalizing to unseen, new environments when trained in one scenario (e.g., lab) and tested in another (e.g., office), including in the cabin of a car.

We present a deep learning-based approach called DipSAR for reconstructing millimeter-wave synthetic aperture radar (SAR) images from sparse samples. The primary challenge lies in the requirement of a large training dataset for deep learning schemes. To overcome this issue, we employ the deep image prior (DIP) technique, which eliminates the need for a large dataset and instead utilizes only the sparse sample itself. Our proposed DipSAR model recovers missing samples from sparse data and reconstructs the SAR image using a conventional method. In this study, we utilize an existing SAR dataset and create fourteen different patterns to generate additional sparse samples by removing certain data points. We then evaluate the performance of DipSAR in comparison to the conventional method. The results show that DipSAR outperforms the conventional method in terms of the intersection over union (IoU) score.