micro-Doppler Research Articles

SCRP-Radar: Space-Aware Coordinate Representation for Human Pose Estimation Based on SISO UWB Radar

Human pose estimation (HPE) is an integral component of numerous applications ranging from healthcare monitoring to human-computer interaction, traditionally relying on vision-based systems. These systems, however, face challenges such as privacy concerns and dependency on lighting conditions. As an alternative, short-range radar technology offers a non-invasive, lighting-insensitive solution that preserves user privacy. This paper presents a novel radar-based framework for HPE, SCRP-Radar (space-aware coordinate representation for human pose estimation using single-input single-output (SISO) ultra-wideband (UWB) radar). The methodology begins with clutter suppression and denoising techniques to enhance the quality of radar echo signals, followed by the construction of a micro-Doppler (MD) matrix from these refined signals. This matrix is segmented into bins to extract distinctive features that are critical for pose estimation. The SCRP-Radar leverages the Hrnet and LiteHrnet networks, incorporating space-aware coordinate representation to reconstruct 2D human poses with high precision. Our method redefines HPE as dual classification tasks for vertical and horizontal coordinates, which is a significant departure from existing methods such as RF-Pose, RF-Pose 3D, UWB-Pose, and RadarFormer. Extensive experimental evaluations demonstrate that SCRP-Radar significantly surpasses these methods in accuracy and robustness, consistently exhibiting lower average error rates, achieving less than 40 mm across 17 skeletal key-points. This innovative approach not only enhances the precision of radar-based HPE but also sets a new benchmark for future research and application, particularly in sectors that benefit from accurate and privacy-preserving monitoring technologies.

Hand gesture recognition based on micro‐Doppler radar using graph neural network

AbstractHand gesture recognition based on micro‐Doppler (MD) radar has garnered considerable attention from researchers as a potential method for human–computer interaction (HCI). However, two significant challenges, that is, obvious differences in MD map size and lots of redundant information contained in the MD maps, are encountered. Here, a multi‐scale graph‐based hand gesture recognition framework is proposed. First, a MD graph representation method is developed, which is adapted to arbitrary‐sized frames and enables to map the key features in a sparse manner. Then, the multi‐scale information from MD graph is fully extracted for hand gesture recognition. Experimental results show that the proposed framework achieves a state‐of‐the‐art accuracy of 96.73% on the four‐class radar hand gesture dataset, while reducing up to 99% of the redundant information in the MD maps. This framework requires only a little amount of memory storage with good hand gesture recognition capability, demonstrating its high potential in the HCI field.

MD-Pose: Human Pose Estimation for Single-Channel UWB Radar

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.

Human Activity Recognition Based on Continuous-Wave Radar and Bidirectional Gate Recurrent Unit

The technology for human activity recognition has diverse applications within the Internet of Things spectrum, including medical sensing, security measures, smart home systems, and more. Predominantly, human activity recognition methods have relied on contact sensors, and some research uses inertial sensors embedded in smartphones or other devices, which present several limitations. Additionally, most research has concentrated on recognizing discrete activities, even though activities in real-life scenarios tend to be continuous. In this paper, we introduce a method to classify continuous human activities, such as walking, running, squatting, standing, and jumping. Our approach hinges on the micro-Doppler (MD) features derived from continuous-wave radar signals. We first process the radar echo signals generated from human activities to produce MD spectrograms. Subsequently, a bidirectional gate recurrent unit (Bi-GRU) network is employed to train and test these extracted features. Preliminary results highlight the efficacy of our approach, with an average recognition accuracy exceeding 90%.

Micro-Doppler Extraction for Cone-Shaped Target Using Constrained Nonnegative Matrix Factorization

Micro-Doppler (MD) extraction is the precondition for target classification based on micro-motion. This letter proposes a method for MD extraction based on constrained nonnegative matrix factorization (NMF). The proposed algorithm is applied to the time-frequency spectrogram of the cone-shaped precession target. Taking the sparsity constraint, temporal continuity constraint and approximate orthogonality constraint into consideration, the basic NMF is improved to match the MD separation problem, and thus the time-frequency spectrogram of each scattering center is reconstructed by deriving the basis matrix and coefficient matrix. Morphological processing, valid frequency extraction and MD curve fitting are conducted on the reconstructed spectrograms to extract the MD accurately. Experiment results show that the proposed approach outperforms other algorithms with better accuracy and robustness.

Dual-Scale Doppler Attention for Human Identification.

This paper considers a Deep Convolutional Neural Network (DCNN) with an attention mechanism referred to as Dual-Scale Doppler Attention (DSDA) for human identification given a micro-Doppler (MD) signature induced as input. The MD signature includes unique gait characteristics by different sized body parts moving, as arms and legs move rapidly, while the torso moves slowly. Each person is identified based on his/her unique gait characteristic in the MD signature. DSDA provides attention at different time-frequency resolutions to cater to different MD components composed of both fast-varying and steady. Through this, DSDA can capture the unique gait characteristic of each person used for human identification. We demonstrate the validity of DSDA on a recently published benchmark dataset, IDRad. The empirical results show that the proposed DSDA outperforms previous methods, using a qualitative analysis interpretability on MD signatures.

Use of ICA to Separate Micro-Doppler Signatures in ISAR Images of Aircraft That Has Fast-Rotating Parts

In this article, we propose a new signal-decomposition algorithm composed of three steps: principal components analysis to yield high-resolution range profiles with improved signal-to-noise ratio; estimation of whitening and mixing matrices using independent component analysis in distributed radar network; and signal decomposition to obtain inverse synthetic aperture radar (ISAR) images that correspond to rigid body and fast-rotating parts by using estimated two matrices regardless of complicated range migration. The proposed method efficiently removes image blur caused by micro-Doppler (MD) signals of rotating parts and reduces the sensitivity to noise. In simulations, our proposed method could perform accurate and robust removal of the MD signatures to obtain a focused ISAR image of an aircraft with fast-rotating parts.

Federated learning over wireless backhaul for distributed micro‐Doppler radars: Deep learning aided gradient estimation

When micro-Doppler (MD) radars are distributed, a federated learning strategy over wireless backhaul links is developed for motion classification. Specifically to identify the human motion, a common convolutional neural network (CNN) model is shared for all the distributed radars (i.e. clients) and it is trained through the federated learning strategy over wireless backhaul connected to the main server. In the proposed system, a main bottleneck is the estimation of local gradients for CNN training at the server, which are transferred from distributed radars over the wireless backhaul link. To overcome it, a deep learning (DL) aided gradient estimation algorithm is proposed, in which the deep neural networks (DNNs) for encoding local gradient vectors at the distributed radars and the DNN for decoding (i.e. estimating) them at the server are jointly trained in an end-to-end autoencoder-based learning strategy. To avoid the inter-client interference over the wireless backhaul link, the DNN structure for the gradient estimation algorithm with the orthogonal multiple access is proposed, in which the proposed DNN effectively learns the encoding/decoding at the transceiver over wireless backhaul. By exploiting the experimental data measured through the USRP-based MD radars, the authors validate the motion classification performance of the proposed federated learning strategy and DL aided gradient estimation over the wireless backhaul link.

Micro-Doppler Extraction of Radar Targets With Translational Motion Based on Spatial Transformer Network

The micro-Doppler (MD) signature, an effect that is induced by micro-motion, characterizes the rich motion information of targets, making it a valuable tool for target classification and recognition. However, the MD structure is always destroyed for radar targets with translational motion. In this letter, we propose an end–to–end translational motion compensation network based on a spatial transformer network (STN). First, the effect of the translational motion on the radar echo and its time-frequency graph (TFG) is modeled, so that translational motion compensation is regarded as an image spatial transformation task. Then, a two-branch convolutional network based on residual modules is designed to locate the transformation parameters, and the TFG containing only MD is obtained using a grid generator and sampler. Finally, the simulation results demonstrate that the proposed algorithm has high accuracy and stability.

Efficient Protocol to Use FMCW Radar and CNN to Distinguish Micro-Doppler Signatures of Multiple Drones and Birds

Classification of multiple drones and birds by comparing micro-Doppler (MD) signatures is a very difficult task because the MD signatures of multiple birds can contaminate the MD signature of drones, and because a single drone can yield a very similar MD signature to that of multiple drones. In this paper, assuming a real observation scenario, we propose three protocols and analyze their accuracy in classification of multiple drones and birds by using the frequency modulated continuous wave radar and a convolutional neural network classifier. In simulations using models of rotating blades and of flapping wings, the method that uses training data that include combinations of drone and bird achieved an accuracy ~100% for the majority vote classification; this result demonstrates that ours is the most appropriate method to distinguish multiple drones from birds.

Efficient Classification of Birds and Drones Considering Real Observation Scenarios Using FMCW Radar

In this study, we consider real observation scenarios and propose an efficient method to accurately distinguish drones from birds using features obtained from their micro-Doppler (MD) signatures. In the simulations conducted using a rotating-blade model and a flapping-wing model, the classification result degraded significantly due to the diversity of both drones and birds, but a combination of features obtained for longer observation times significantly improved the accuracy. MD bandwidth was found to be the most efficient feature, but sufficient observation time was required to exploit the period of time-varying MD as a useful feature.

Analysis of Micro-Doppler Signatures of Small UAVs Based on Doppler Spectrum

Most of the investigations on the micro-Doppler (MD) effect caused by a small unmanned aerial vehicle (UAV) have been conducted using joint time–frequency (JTF) images rather than the Doppler spectrum. On the other hand, several researchers utilized the Doppler spectrum instead of JTF images to observe the MD signature of a small UAV, and found the relationship between the spectral distribution of a small UAV and its physical specifications. However, the studies using the Doppler spectrum still lack concrete and theoretical foundations of the MD effects of a small UAV, focusing mainly on phenomena identified by measurement data. In this article, we establish the theoretical foundation connecting the MD signatures and motion dynamics of small UAVs based on the Doppler spectrum, and analyze their spectral distribution using simulations and measured data. In addition, experimental analysis is conducted using the data measured from various types of small UAVs considering the translational motion and aspect change. In contrast to already existing investigations, we completely explain and predict the changes on the Doppler spectrum relative to the physical specifications of a small UAV (e.g., blade length and rotor rotation rate). In particular, we show that the Doppler spectrum, compared to the JTF images, is a considerably simple and useful tool for analyzing the MD effects of small flying UAVs. The analysis results reveal that the MD features obtained from the measured echoes of small UAVs have considerable potential for detection and classification of small UAVs.

Linear Discriminant Analysis-Based Motion Classification Using Distributed Micro-Doppler Radars with Limited Backhaul.

In this paper, we propose a cooperative linear discriminant analysis (LDA)-based motion classification algorithm for distributed micro-Doppler (MD) radars which are connected to a data fusion center through the limited backhaul. Due to the limited backhaul, each radar cannot report the high-dimensional data of a multi-aspect angle MD signature to the fusion center. Instead, at each radar, the dimensionality of the MD signature is reduced by using the LDA algorithm and the dimensionally-reduced MD signature can be collected at the data fusion center. To further reduce the burden of backhaul, we also propose the softmax processing method in which the distances of the sensed MD signatures from the centers of clusters for all motion candidates are computed at each radar. The output of the softmax process at each radar is quantized through the pyramid vector quantization with a finite number of bits and is reported to the data fusion center. To improve the classification performance at the fusion center, the channel resources of the backhaul are adaptively allocated based on the classification separability at each radar. The proposed classification performance was assessed with synthetic simulation data as well as experimental data measured through the USRP-based MD radar.

Classification of Space Micromotion Targets With Similar Shapes at Low SNR

There are a number of targets in space that are similar in shape, but they pose different threat levels. It is necessary to discriminate these targets for the sake of space security. The unique characteristics of detailed micro-Doppler (MD) signatures can be used to classify micromotion forms, such as spinning, precession, nutation, wobbling, and tumbling. In this letter, we design a novel processing flow for classifying five types of micromotion under low signal-to-noise ratio (SNR) condition. We start from the range map and successively perform noise-level estimation, adaptive filtering, and multithreshold segmentation to obtain an ideal binary mask. The clean range maps are used as the final input to an eight-layer convolutional neural network (CNN) for training, validation, and testing. The experiment results show that our method achieves more than 80% classification accuracy for micromotion in the case of −10 dB, which is higher than the existing popular methods.

Human Motion Recognition With Limited Radar Micro-Doppler Signatures

The performance of deep learning (DL) algorithms for radar-based human motion recognition (HMR) is hindered by the diversity and volume of the available training data. In this article, to tackle the issue of insufficient training data for HMR, we propose an instance-based transfer learning (ITL) method with limited radar micro-Doppler (MD) signatures, alleviating the burden of collecting and annotating a large number of radar samples. ITL is a unique algorithm that consists of three interconnected parts, including DL model pretraining, correlated source data selection, and adaptive collaborative fine-tuning (FT). Any of the three components cannot be excluded; otherwise, the performance of the entire algorithm decreases. The experiments with a radar data set of six human motions show that ITL achieves state-of-the-art performance for HMR with limited training samples, outperforming several existing transfer learning approaches. Especially, when there are only 100 samples per person per class, ITL yields an F1 score of 96.7%. Last but not least, ITL is more generalized to human motion differences. Though adapted to recognize the persons’ motions in a small-scale target data set, ITL can also classify the persons’ motion data used for pretraining, achieving up to 11.0% F1 score enhancement over the conventional FT method.

Sign Language/Gesture Recognition Based on Cumulative Distribution Density Features Using UWB Radar

It is an important topic to effectively perceive the gestures of the elderly and patients, communicate conveniently with the deaf and dumb, and accurately transmit abnormal alarm information through the network, which would have vast applications in the future. Seamlessly communicating among people, including vulnerable groups, is an important sign of social civilization and technological progress. As an important sensor, micro-Doppler (MD) radar has many advantages such as not being restricted by ambient light, long detection distance, no need to consider occlusion, and sensitive detection to slight movements. In recent years, it has been used in various recognition tasks. In this article, we proposed a sign language (SL)/hand gesture recognition method based on a novel discriminative feature, built a measurement system of hand movements using an ultrawideband (UWB) radar, measured ten-type, 15-type SL actions, and ten-type hand gesture actions, and completed the tasks of ten-type SL/hand gesture recognition based on the extracted new features. Compared with the five related methods, experimental results show that the proposed method improves the recognition accuracy by 6.4% on the ten-type SL dataset, which proves the effectiveness of the new cumulative distribution density (CDD) features. For comparison, the proposed method is directly applied to the ten-type hand gesture dataset without any change, and recognition accuracy is improved by 8.6%, which verifies the universality of the proposed new CDD features. Meanwhile, we also give the experimental results when the radial distance measured by radar changes and the types of SL increase.

Arm Motion Classification Using Time-Series Analysis of the Spectrogram Frequency Envelopes

Hand and arm gesture recognition using radio frequency (RF) sensing modality proves valuable in man–machine interfaces and smart environments. In this paper, we use the time-series analysis method to accurately measure the similarity of the micro-Doppler (MD) signatures between the training and test data, thus providing improved gesture classification. We characterize the MD signatures by the maximum instantaneous Doppler frequencies depicted in the spectrograms. In particular, we apply two machine learning (ML) techniques, namely, the dynamic time warping (DTW) method and the long short-term memory (LSTM) network. Both methods take into account the values as well as the temporal evolution and characteristics of the time-series data. It is shown that the DTW method achieves high gesture classification rates and is robust to time misalignment.

Automatic Arm Motion Recognition Based on Radar Micro-Doppler Signature Envelopes

In considering human-machine interface (HMI) for smart environment, a simple but effective method is proposed for automatic arm motion recognition with a Doppler radar sensor. Arms, in lieu of hands, have stronger radar cross-section and can be recognized from relatively longer distances. An energy-based thresholding algorithm is applied to the spectrograms to extract the micro-Doppler (MD) signature envelopes. The positive and negative frequency envelopes are concatenated to form a feature vector. The nearest neighbor (NN) classifier with Manhattan distance (L1) is then used to recognize the arm motions. It is shown that this simple method yields classification accuracy above 97 percent for six classes of arm motions. Despite its simplicity, the proposed method is superior to those of handcrafted featurebased classifications and low-dimension representation techniques based on principal component analysis (PCA), and is comparable to convolutional neural network (CNN).

Micro-Doppler Curves Extraction Based on High-Order Particle Filter Track-Before Detect

Micro-Doppler (MD) radar signatures characterize rich motion information of the targets and are of great significance in target recognition. In this letter, we propose a novel high-order particle filter track-before-detect (PF-TBD) approach for the MD curves extraction. In the proposed approach, the sinusoidal Doppler frequency curve is treated as the state, whose dynamic model is described as a high-order Markov chain. First, the state equation is divided into two parts, the translational motion part represented as a first-order dynamic process and the micromotion part represented as a high-order dynamic process including static model parameters. Then, a kernel smoothing approach is introduced for the static model parameters estimation, and the auxiliary particle filter (APF) is utilized for the instantaneous Doppler curves extraction. Finally, the experiments on the electromagnetic analysis data are carried out to validate the performance of the proposed method.

Separate estimation schemes for time-frequency overlapped micro-doppler signal in single channel infrared laser detection

The micro-Doppler (MD) effect of weak vibration target is obvious in infrared laser detection. This provides the foundation for precise estimation of micro-motion parameters, and makes the target classification and recognition possible. The multi-targets or multi-scattering points existing in the detecting field will generate the single-channel multi-component (SCMC) signal in laser detection. Further, the similar micro-motion parameters will lead to the feature overlapping in time-frequency domain, which will increase the difficulty of parameter estimation. In this paper, a separate parameter estimator based on the maximum likelihood framework and singular value decomposition is proposed to deal with this mixed signal. First, an improved singular value ratio (SVR) spectrum with detailed period scanning is presented to locate the vibration frequency. The amplitude ratio information of each component is also extracted from the SVR spectrum. Then, the analytic expression of the maximum likelihood estimation (MLE) of micro-motion parameters is derived. To solve the high nonlinear problem in laser MD signal, a new likelihood function (LF) is designed in the derivation process. The Robustness and efficiency are both increased with this new LF. The Markov chain Monte Carlo (MCMC) sampling is employed to implement the MLE. Finally, the simulation results verifies the validity of the proposed method. The comparison with the Cramer-Rao bound shows the ability of accurate estimation of the proposed method.