The problem of radar-based multi-target tracking for indoor human monitoring is considered. Tracking and counting the number of people moving as a group is particularly challenging as multiple individuals are close together and their radar signatures are mixed. A transformer-based classification approach for counting the number of grouped people is proposed. The Neural Network model is trained with selected features from the spatial domain and Doppler frequency domain, which are concatenated over multiple frames to form a sequence for the transformer network. Compared to statistical classifiers, the self-attention mechanism allows transformers to capture feature long-term dependencies. The proposed classifier is integrated into a tracking pipeline in order to monitor the position and number of people in the grouped targets. The method proposed is experimentally verified using a 24 GHz Frequency Modulated Continuous Wave (FMCW) radar with 250MHz bandwidth. Despite the relatively coarse range resolution, the proposed method achieves 92.5% accuracy in these initial tests. Furthermore, the method performances and related accuracy is analyzed according to various parameters.

In this paper the problem of tracking multiple people in an indoor environment is formulated and analyzed with the usage of a Multiple Input Multiple Output (MIMO) Frequency Modulated Continuous Wave (FMCW) radar. The objective is to evaluate the performance of FMCW MIMO radar with relatively limited bandwidth for accurately tracking single and multiple individuals in various scenarios. Three different tracking approaches are compared and quantitatively analyzed with single and multi-target scenarios. As the angular resolution is significant for distinguishing multiple targets, the effect of the number of MIMO channels used is compared among different trackers. The performance is analyzed by metrics of distance error and cardinality error in track association/assignment.

The problem of 3D ego-motion velocity estimation using multichannel Frequency Modulated Continuous Wave (FM CW) radar sensors has been studied. Special attention is given to presence of moving targets in the scene. These targets are first distinguished by the difference between the measured Doppler, and the Doppler calculated with an initial rough estimation of the vehicle ego-velocity. Then, an iterative algorithm is proposed to reduce the influence of the moving targets in the ego-motion estimation procedure, thus improving the overall accuracy. The performance of the proposed algorithm is compared with state-of-the-art alternatives based on simulated data, and superior performance has been demonstrated.

A feasibility study to detect the respiratory rate and heart rate of primates using non-invasive contactless radar sensors and a dedicated processing pipeline is performed. The proposed approach is validated using measurement data from an individual bonobo, simultaneously collected by two different types of radar sensors (pulsed Ultra Wide Band and FMCW, operating at different frequencies). The results show that it is feasible to infer both respiration and heart rate data from the radar sensors.

In this paper, the problem of formulating effective processing pipelines for indoor human tracking is investigated, with the usage of a Multiple Input Multiple Output (MIMO) Frequency Modulated Continuous Wave (FMCW) radar. Specifically, two processing pipelines starting with detections on the Range-Azimuth (RA) maps and the Range-Doppler (RD) maps are formulated and compared, together with subsequent clustering and tracking algorithms and their relevant parameters. Experimental results are presented to validate and assess both pipelines, using a 24 GHz commercial radar platform with 250 MHz bandwidth and 15 virtual channels. Scenarios where 1 and 2 people move in an indoor environment are considered, and the influence of the number of virtual channels and detectors' parameters is discussed. The characteristics and limitations of both pipelines are presented, with the approach based on detections on RA maps showing in general more robust results.

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.