Human pose reconstruction has been a fundamental research in computer vision. However, existing pose reconstruction methods suffer from the problem of wall occlusion that cannot be solved by a traditional optical sensor. This article studies a novel human target pose reconstruction framework using low-frequency ultra-wideband (UWB) multiple-input multiple-output (MIMO) radar and a convolutional neural network (CNN), which is used to detect targets behind the wall. In the proposed framework, first, we use UWB MIMO radar to capture the human body information. Then, target detection and tracking are used to lock the target position, and the back-projection algorithm is adopted to construct three-dimensional (3D) images. Finally, we take the processed 3D image as input to reconstruct the 3D pose of the human target via the designed 3D CNN model. Field detection experiments and comparison results show that the proposed framework can achieve pose reconstruction of human targets behind a wall, which indicates that our research can make up for the shortcomings of optical sensors and significantly expands the application of the UWB MIMO radar system.

As the size of the radar hardware platform becomes smaller and smaller, the cost becomes lower and lower. The application of indoor radar-based human motion recognition has become a reality, which can be realized in a low-cost device with simple architecture. Compared with narrow-band radar (such as continuous wave radar, etc.), the human motion echo signal of the carrier-free ultra-wideband (UWB) radar contains more abundant characteristic information of human motion, which is helpful for identifying different types of human motion. In this paper, a novel feature extraction method by two-dimensional variational mode decomposition (2D-VMD) algorithm is proposed. And it is used for extracting the primary features of human motion. The 2D-VMD algorithm is an adaptive non-recursive multiscale decomposition method for nonlinear and nonstationary signals. Firstly, the original 2D radar echo signals are decomposed by the 2D-VMD algorithm to capture several 2D intrinsic mode function (BIMFs) which represent different groups of central frequency components of a certain type of human motion. Secondly, original echo signals are reconstructed according to the several BIMFs, which not only have a certain inhibitory effect on the clutter in the echo signal, but can also further demonstrate that the BIMFs obtained by the 2D-VMD algorithm can represent the original 2D echo signal well. Finally, based on the measured ten different types of UWB radar human motion 2D echo analysis signals, the characteristics of these different types of human motion are extracted and the original echo signal are reconstructed. Then, the three indicators of the PCC, UQI, and PSNR between the original echo signals and extraction/reconstruction 2D signals are analyzed, which illustrate the effectiveness of 2D-VMD algorithm to extract feature of human motion 2D echo signals of the carrier-free UWB radar. Experimental results show that BIMFs by 2D-VMD algorithm can well represent the echo signal characteristics of this type of human motion, which is a very effective tool for human motion radar echo signal feature extraction.

Compressive Sensing (CS) technology has been widely applied in synthetic aperture radar (SAR) imaging. However, the typical sparse reconstruction model, L ₀ regularization, can overlook sparse features in target cluster structures, leading to reconstructed SAR images with isolated scatterers and making it impossible to discern the underlying target structure. Furthermore, the iterative hard thresholding (IHT) method shows great potential in sparse imaging. However, it requires transforming the echo data and imaging grid matrixs into vectors and constructing a large observation matrix. This leads to a significant computational overhead in large-scale scenes. In this letter, we propose a convolutional reweighting L0 norm regularization method, rooted in structural sparsity within CS theory. This method aims to enhance the sparse features of cluster structures in radar images. To expedite computation, we introduce the Armijo gradient descent line-search step size criterion to adapt the step size in the iteration process and the imaging support set. Ultimately, two sets of experimental data demonstrate the algorithm's accuracy and evaluate its viability.

Human pose estimation based on optical sensors is difficult to solve the situation under harsh environments and shielding. In this paper, a Micro-Doppler (MD) based human pose estimation for the single-channel ultra-wideband (UWB) radar, called MD-Pose, is proposed. The MD characteristic reflects the kinematics of the human and provides a unique method for identifying the targets posture, which offers a more comprehensive perception of human posture. We explore the relationship between the human skeleton and the MD signature, which reveals the fundamental origins of these previously unexplained phenomena. The single-channel UWB radar is widely used because of its small size, low cost, and portability. In contrast, its resolution is lower than that of the MIMO UWB radar. Therefore, this paper reveals how to implement fine-grained human posture based on the MD signature with fewer channels. The MD spectrogram of the human target is obtained by the short-time Fourier transform (STFT), which is the input data of the proposed MD-Pose. A quasi-symmetric U-Net neural network is trained with the UWB radar MD spectrogram, which can estimate the human keypoints. The experiments show comparable quantitative results with the state-of-the-art human pose estimation method and provide the underlying insights needed to guide the design of radar-based human pose estimation.

In this paper, we propose an innovative approach for transforming 2D human pose estimation into 3D models using Single Input–Single Output (SISO) Ultra-Wideband (UWB) radar technology. This method addresses the significant challenge of reconstructing 3D human poses from 1D radar signals, a task traditionally hindered by low spatial resolution and complex inverse problems. The difficulty is further exacerbated by the ambiguity in 3D pose reconstruction, as multiple 3D poses may correspond to similar 2D projections. Our solution, termed the Radar PoseLifter network, leverages the micro-Doppler signatures inherent in 1D radar echoes to effectively convert 2D pose information into 3D structures. The network is specifically designed to handle the long-range dependencies present in sequences of 2D poses. It employs a fully convolutional architecture, enhanced with a dilated temporal convolutions network, for efficient data processing. We rigorously evaluated the Radar PoseLifter network using the HPSUR dataset, which includes a diverse range of human movements. This dataset comprises data from five individuals with varying physical characteristics, performing a variety of actions. Our experimental results demonstrate the method’s robustness and accuracy in estimating complex human poses, highlighting its effectiveness. This research contributes significantly to the advancement of human motion capture using radar technology. It presents a viable solution for applications where precision and reliability in motion capture are paramount. The study not only enhances the understanding of 3D pose estimation from radar data but also opens new avenues for practical applications in various fields.

This paper introduces the Human Pose Estimation based on the Single-Input Single-Output (SISO) Ultra-Wideband (UWB) Radar (HPSUR) benchmark, a pioneering approach in human pose estimation integrating motion capture technology based on SISO UWB radar sensors. The HPSUR dataset, consisting of 311,963 data frames, was meticulously assembled using cross-calibrated SISO UWB radar sensors in conjunction with the Noitom Perception Neuron 3 (N3), specifically designed for radar-based human pose estimation. This dataset captures diverse movements from five subjects of varying physical characteristics, performing four distinct categories of actions. In addition to establishing this comprehensive benchmark, our research proposes an innovative framework for 2D human pose estimation based on SISO UWB radar. The framework leverages the processing of Micro Doppler (MD) signatures through a unique combination of cascade and parallel Swin Transformers. The MD signatures, reflective of human kinematics, form the basis for a novel methodology in posture identification, thus enhancing the perception of human postures. Addressing the challenge of managing long-range dependencies due to the high sampling rates of radar devices, we introduce the Micro Doppler Swin Transformer (MDST) network. This novel transformer incorporates Window-based Multi-head Self-Attention (W-MSA) and Shifted Window-based Multi-head Self-Attention (SW-MSA) models to capture the inner-frame and intra-frame aspects of the MD signature adeptly. Furthermore, the study integrates an Inverted Feature Pyramid Network (IFPN) for an efficient multi-scale feature representation, enriching the feature pyramid with high-level semantics. Our extensive experimental analysis, conducted on the HPSUR benchmark, demonstrates the significant enhancement in the accuracy of human pose estimation offered by the proposed MDST network. This improvement is consistently observed across six MDST variants under various conditions involving diverse subjects and postures, showcasing the robustness and generalizability of our approach.