Radar technology offers much appeal for contactless vital sign monitoring. While most radar-based approaches achieve reasonable performance for single-person scenarios, they suffer from inaccurate vital sign estimates for multiple individuals, especially when the subjects occupy the same range bin. In addition, complex interferences due to environmental clutter and random body movements (RBMs) further exacerbate the issue of poor performance. We propose a resonance-based sparse separation (RBSS) algorithm for contactless vital sign measurement using millimeter wave (mmWave) multiple-input multiple-output (MIMO) radar. The proposed algorithm utilizes the resonance-based signal decomposition and can achieve reliable reconstruction of cardiopulmonary signals and precise estimates of respiration rate (RR) and heart rate (HR) for multiple individuals located within the same range bin. Experiment results demonstrate the efficacy of the proposed method, even under conditions of heavy clutter and moderate RBMs.

We propose a complex LeDIM-net (C-LeDIM-net) convolutional neural network (CNN) that employs a newly-formulated complex phasor normalization for gridless direction-of-arrival (DOA) estimation. Unlike existing deep learning (DL) approaches, C-LeDIM-net extracts explicit phase information in its intermediate complex-valued feature maps to estimate unknown source DOAs. Given its explicit phase representation, the proposed complex phasor normalization leverages the phase-to-sensor relationship of the feature maps which, as a consequence, improves the robustness of C-LeDIM-net to array imperfections when operating with limited number of snapshots. Simulation results show that the proposed method outperforms the existing methods, including the subspace-based and DL-based methods.

This work addresses the source localization and association problem in a multipath propagation environment. By focusing on the limitation of the prior information in practical applications, we propose a target localization and association method based on iterative optimization with semi-unitary constraint and eigen-decomposition techniques. In contrast to the previous works, the proposed method can localize spatial sources and associate the incident paths to each source without prior knowledge pertaining to the propagation environment. Moreover, the proposed approach can be applied to an arbitrary array geometry without reducing the effective array aperture. Both simulations and real data experiments validate the effectiveness and robustness of the proposed method.

Pre-trained single-channel neural networks have become more prevalent for noise reduction in recent years. However, unlike their multichannel counterparts, these monoaural approaches do not exploit spatial information during the optimization process. Furthermore, while multichannel neural networks exploit spatial information, they are optimized for a specific microphone array configuration; extensive data collection and training are required if a new array configuration is deployed. We propose a transfer learning approach that leverages existing pre-trained single-channel neural networks for the optimization of multichannel neural networks. Simulation results on the CHiME-3 dataset show that the proposed method outperforms the state-of-the-art multichannel neural network and neural beamformer.

For the problem of target localization under the multipath propagation environment, the existing methods are mainly restricted to the limited prior information of complex reflections, especially when the target is embedded in a mixed interference environment. They may suffer from performance degradation due to the shortage of target classification ability. To address this problem, we propose a target localization method based on iterative implementation with semiunitary constraint and eigen-decomposition technique, where a practical propagation scenario based on the spherical Earth model is considered. Compared to the previous works, the proposed method can automatically distinguish a real target from the mixed interference environment with improved localization accuracy. Neither additional decorrelation preprocessing nor prior information of the dynamic scenario is required. Both simulations and real data experiments validate the effectiveness and robustness of the proposed method.

In a multipath propagation environment, prevalent target localization methods are mainly based on the classical two-ray propagation model without considering other reflected waves. Because the received target echoes are considerably corrupted by multipath reflections in the case of complex terrain, these prevalent methods might fail to work or achieve poor performance. To solve this problem, we first consider a practical multipath propagation scenario to reveal the dynamic structural relationship of the spatial paths based on the spherical earth model. Subsequently, a target localization algorithm based on low-rank decomposition (LRD) and sparse representation (SR) framework is proposed. The proposed algorithm can effectively mitigate the effects of complex multipath interference without using any prior knowledge on the illuminated terrain and the reflecting paths. Experiments on synthetic data and real data validate the effectiveness of the proposed algorithm.

For modern meter-wave radar, the performance of low-angle target altitude measurement is limited by multipath phenomenon, especially in the complex terrain environment where the multipath signal is perturbed by irregular surface. To address this problem, a practical signal model for meter-wave radar in practical terrain is first presented, where the influence of the perturbed multipath caused by irregular reflecting surface is taken into consideration. A novel compressive sensing (CS) based altitude measurement algorithm, combined with alternative optimization and dictionary updating techniques, is then proposed, in which the perturbation caused by the complex terrain can be iteratively compensated to estimate the target altitude more precisely. Numerical results based on both simulated data and real data demonstrate the effectiveness of the proposed algorithm under complex terrain environment.