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In the field of autonomous driving, it is important to protect vulnerable road users (VRUs) and ensure the safety of autonomous driving effectively by improving the detection accuracy of VRUs in the driver's field of vision. However, due to the strong temporal similarity between pedestrians and cyclists, the insensitivity of the traditional least squares method to their differences results in its suboptimal classification performance. In response to this issue, this paper proposes an algorithm for classifying pedestrian and cyclist targets based on the micro-Doppler effect. Firstly, distinct from conventional time-frequency fusion methods, a preprocessing module was developed to solely perform frequency-domain fitting on radar echo data of pedestrians and cyclists in forward motion, with the purpose of generating fitting coefficients for the classification task. Herein, wavelet threshold processing, short-time Fourier transform, and periodogram methods are employed to process radar echo data. Then, for the heightened sensitivity to inter-class differences, a fractional polynomial is introduced into the extraction of micro-Doppler characteristics of VRU targets to enhance extraction precision. Subsequently, the support vector machine technique is embedded for precise feature classification. Finally, subjective comparisons, objective explanations, and ablation experiments demonstrate the superior performance of our algorithm in the field of VRU target classification. [ABSTRACT FROM AUTHOR]

Human falls occur rarely; however, they can lead to severe consequences if not addressed immediately. With the rise of nuclear families, there has been a significant increase in the risk of unnoticed falls leading to even death. Therefore, an efficient fall monitoring system is essential for prompt assistance and minimizing fall risks. Several strategies have been proposed with technological advancements, and some devices are already available on the market. Among the proposed systems, radar technology has emerged as a state-of-the-art noninvasive technique due to its attractive and unique features, such as the ability to operate under occlusion, dirt, and fog without compromising the person's privacy. This paper presents a systematic review of a state-of-the-art fall detection system based on radar technology. While various reviews on fall-related technologies exist, none specifically focus on radar-based systems. To our knowledge, we are the first to provide a review in such detail of state-of-the-art radar sensor-based fall detection. We comprehensively outline the radar-based fall monitoring system and the corresponding data processing techniques. Finally, we conclude our paper by discussing current trends, future research direction and challenges in this research field. This paper aims to help researchers in the field of noninvasive fall detection and facilitate future research. [ABSTRACT FROM AUTHOR]

The imaging of aerial targets using Inverse Synthetic Aperture Radar (ISAR) is affected by micro-Doppler effects resulting from localized micromotions, such as rotation and vibration. These effects introduce additional Doppler frequency modulation into the echo, leading to spectral broadening. Under ultrahigh-resolution conditions, these micromotions interfere with the focusing process of subject scatterers, resulting in images with poor focus showing significantly reduced quality. Furthermore, micro-Doppler signals exhibit temporal variability and nonstationary characteristics, posing difficulties in their estimation and differentiation from the echo. To address these challenges, this paper proposes a nonparametric method based on Variational Mode Decomposition (VMD) and mode optimization to separate the echo of the subject from micro-Doppler components. This separation is achieved by utilizing differences in their respective time-frequency distributions. This methodology mitigates the effect of micro-Doppler signals on the echo and obtains imaging results of a drone with ultrahigh-resolution. The VMD algorithm is introduced and subsequently extended to the complex domain. The method entails the decomposition of the ISAR echo along the azimuth direction into several mode functions distributed uniformly across the Doppler sampling bandwidth. Subsequently, image entropy indices are employed to optimize the decomposition parameters and select the imaging modes. This ensures the effective suppression of micro-Doppler signals and preservation of the subject echo. Compared to existing methods based on Empirical Mode Decomposition (EMD) and Local Mean Decomposition (LMD), the proposed method exhibits superior performance in suppressing image blurring caused by micro-Doppler effects while ensuring complete retention of fuselage details. Furthermore, the effectiveness and advantages of the proposed method are validated through simulations and processing of ultrawideband microwave photonic data obtained from drone measurements.

Human action recognition based on optical and infrared video data is greatly affected by the environment, and feature extraction in traditional machine learning classification methods is complex; therefore, this paper proposes a method for human action recognition using Frequency Modulated Continuous Wave (FMCW) radar based on an asymmetric convolutional residual network. First, the radar echo data are analyzed and processed to extract the micro-Doppler time domain spectrograms of different actions. Second, a strategy combining asymmetric convolution and the Mish activation function is adopted in the residual block of the ResNet18 network to address the limitations of linear and nonlinear transformations in the residual block for micro-Doppler spectrum recognition. This approach aims to enhance the network's ability to learn features effectively. Finally, the Improved Convolutional Block Attention Module (ICBAM) is integrated into the residual block to enhance the model's attention and comprehension of input data. The experimental results demonstrate that the proposed method achieves a high accuracy of 98.28% in action recognition and classification within complex scenes, surpassing classic deep learning approaches. Moreover, this method significantly improves the recognition accuracy for actions with similar micro-Doppler features and demonstrates excellent anti-noise recognition performance. [ABSTRACT FROM AUTHOR]

In the time-frequency analysis of the echoes from targets with composite micro-motions, there is a problem that the energy of time-frequency strip is difficult to focus, which affects the extraction and analysis of the target's micro-Doppler feature. To solve this problem, this paper proposes the concept of time-frequency harmonic stripes and corresponding time-frequency transformation methods. Based on the assumption that the composite micro-motions include central motion components and high-frequency motion components, firstly, spline function is used to fit the frequency curve of the central motion component, and therefore construct the time-frequency transform kernel is constructed. Then, parameterized time-frequency transform is used to convert the composite micro-Doppler signal in the time domain to harmonic stripes on the time-frequency plane. Compared with traditional methods such as short-time Fourier transform and pseudo Wigner-Ville distribution, the proposed method can significantly improve the energy aggregation and spur suppression of time-frequency transform of radar echoes from composite micro-motion targets. Simulation results verify the effectiveness of the proposed method and its adaptability to low signal-to-noise ratio (SNR) conditions. [ABSTRACT FROM AUTHOR]

Real-time, radar-based human activity and target classification is useful for wide-area ground surveillance. However, the feasibility of deploying deep learning (DL) models in radar-based systems with limited computational resources remains unexplored. This paper investigated the effect of quantization on model throughput and accuracy for deployment in radar systems. A seven-layer residual network was proposed to classify groundmoving targets and achieved a test accuracy of 87.72%. The model was then quantized to 16-bit and 8-bit precision, resulting in a 3.8 times speedup in inference throughput, with less than a 0.4% drop in test and validation accuracy. The results showed that quantization can improve inference throughput with a negligible decrease in target classification accuracy. The increase in throughput and reduction in computational expense that comes with quantization promotes the feasibility of the deployment of DL models in systems with limited computational resources. The findings of this paper hold significant promise for the successful use of quantized models in modern radar systems, while adhering to stringent size, weight and power consumption constraints. [ABSTRACT FROM AUTHOR]

With the continuous advancement of autonomous driving and monitoring technologies, there is increasing attention on non-intrusive target monitoring and recognition. This paper proposes an ArcFace SE-attention model-agnostic meta-learning approach (AS-MAML) by integrating attention mechanisms into residual networks for pedestrian gait recognition using frequency-modulated continuous-wave (FMCW) millimeter-wave radar through meta-learning. We enhance the feature extraction capability of the base network using channel attention mechanisms and integrate the additive angular margin loss function (ArcFace loss) into the inner loop of MAML to constrain inner loop optimization and improve radar discrimination. Then, this network is used to classify small-sample micro-Doppler images obtained from millimeter-wave radar as the data source for pose recognition. Experimental tests were conducted on pose estimation and image classification tasks. The results demonstrate significant detection and recognition performance, with an accuracy of 94.5%, accompanied by a 95% confidence interval. Additionally, on the open-source dataset DIAT-μRadHAR, which is specially processed to increase classification difficulty, the network achieves a classification accuracy of 85.9%. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Motion characterization, including Doppler and micro-Doppler, is crucial for the detection and identification of high-speed underwater targets. Under high-frequency and short-range conditions, underwater targets cannot be simply regarded as single highlight targets as they exhibit a complex structure with multiple scattering centers accompanied by distinct micro-motions. To address this multi-highlight and multi-micro-motion scenario, a model is proposed to characterize the motion features of underwater targets. Firstly, a mathematical model is established to represent the micro-Doppler features based on the single-highlight model. Subsequently, considering the overlap of multiple highlight echoes caused by the high-speed translation of the target and the long pulse detection signal, precise representation is achieved by setting motion positions and calculating time delays within the model. The results represent the echoes of moving targets with multiple highlights and micromotions. Finally, a time-frequency analysis method is employed to extract motion features and estimate target parameters, thereby validating the accuracy and effectiveness of the proposed model. This research provides a theoretical foundation for the modeling of underwater moving targets. [ABSTRACT FROM AUTHOR]

Current methods for fall risk assessment rely on Quantitative Gait Analysis (QGA) using costly optical tracking systems, which are often only available at specialized laboratories that may not be easily accessible to rural communities. Radar placed in a home or assisted living facility can acquire continuous ambulatory recordings over extended durations of a subject's natural gait and activity. Thus, radar-based QGA has the potential to capture day-to-day variations in gait, is time efficient and removes the burden for the subject to come to a clinic, providing a more realistic picture of older adults’ mobility. Although there has been research on gait-related health monitoring, most of this work focuses on classification-based methods, while only a few consider gait parameter estimation. On the one hand, metrics that are accurately and easily computable from radar data have not been demonstrated to have an established correlation with fall risk or other medical conditions; on the other hand, the accuracy of radar-based estimates of gait parameters that are well-accepted by the medical community as indicators of fall risk have not been adequately validated. This paper provides an overview of emerging radar-based techniques for gait parameter estimation, especially with emphasis on those relevant to fall risk. A pilot study that compares the accuracy of estimating gait parameters from different radar data representations – in particular, the micro-Doppler signature and skeletal point estimates – is conducted based on validation against an 8-camera, marker-based optical tracking system. The results of pilot study are discussed to assess the current state-of-the-art in radar-based QGA and potential directions for future research that can improve radar-based gait parameter estimation accuracy.

In the field of autonomous driving, it is important to protect vulnerable road users (VRUs) and ensure the safety of autonomous driving effectively by improving the detection accuracy of VRUs in the driver’s field of vision. However, due to the strong temporal similarity between pedestrians and cyclists, the insensitivity of the traditional least squares method to their differences results in its suboptimal classification performance. In response to this issue, this paper proposes an algorithm for classifying pedestrian and cyclist targets based on the micro-Doppler effect. Firstly, distinct from conventional time-frequency fusion methods, a preprocessing module was developed to solely perform frequency-domain fitting on radar echo data of pedestrians and cyclists in forward motion, with the purpose of generating fitting coefficients for the classification task. Herein, wavelet threshold processing, short-time Fourier transform, and periodogram methods are employed to process radar echo data. Then, for the heightened sensitivity to inter-class differences, a fractional polynomial is introduced into the extraction of micro-Doppler characteristics of VRU targets to enhance extraction precision. Subsequently, the support vector machine technique is embedded for precise feature classification. Finally, subjective comparisons, objective explanations, and ablation experiments demonstrate the superior performance of our algorithm in the field of VRU target classification.

Micro-Doppler signature represents the micromotion state of a target, and it is used in target recognition and classification technology. The micro-Doppler frequency appears as a transition of the Doppler frequency due to the rotation and vibration of an object. Thus, tracking and classifying targets with high recognition accuracy is possible. However, it is difficult to distinguish the types of targets when subdividing targets with the same micromotion or classifying different targets with similar velocities. In this study, we address the problem of classification of three different targets with similar speeds and segmentation of the same type of targets. A novel signature extraction procedure is developed to automatically recognize drone, bird, and human targets by exploiting the different micro-Doppler signatures exhibited by each target. The developed algorithm is based on a novel adaptation of the spectral kurtosis technique of the radar echoes reflected by the three target types. Further, image-embedding layers are used to classify the spectral kurtosis of objects with the same micromotion. We apply a ResNet34 deep neural network to micro-Doppler images to analyze its performance in classifying objects performing micro-movements on the collected bistatic radar data. The results demonstrate that the proposed method accurately differentiates the three targets and effectively classifies multiple targets with the same micromotion. [ABSTRACT FROM AUTHOR]

The extraction of the micro-Doppler (m-D) feature based on time-frequency distribution (TFD) is of great significance for target detection and identification. To improve the feature extraction performance, numerous TFDs have been developed, with the majority falling under Cohen's class. Nevertheless, these TFDs basically face a trade-off between artifact suppression and energy concentration. The main reason is that each Cohen's class TFD is constructed by applying the two-dimensional Fourier transform to a kerneled ambiguity function directly, while existing kernels generally attenuate artifacts at the expense of losing valuable information. In this paper, a TFD reconstruction method employing an adaptive short-time kernel (ASTK) is developed in the framework of sparse representation (SR) theory to overcome this trade-off and enhance the m-D feature. Firstly, the task of the optimal kernel is explained from the viewpoint of the instantaneous auto-correlation function (IAF). Secondly, based on the quasi-linear frequency modulation feature of most m-D signals during short-time periods, the distribution rule of the short-time IAF (STIAF) in the ambiguity plane is concluded. Guided by this rule, an ASTK that can effectively remove unwanted artifacts with the least information loss is designed. Finally, an SR-based reconstruction procedure is conducted on the kerneled STIAF to generate an artifact-free TFD with high energy concentration, which can effectively enhance the m-D feature. Experiments using both simulated and real-world m-D signals demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

To solve the problem of target recognition for flying‐birds and biomimetic flapping‐wing unmanned aerial vehicles (UAVs), a rectangular‐trapezoidal composite scattering model for lidar detection of bird wings has been proposed and established. According to the model, the influence of different line‐of‐sight angles on the time–frequency distribution characteristics of laser micro‐Doppler under the same flapping wing motion was simulated and studied. Then, the flapping‐wing frequency and maximum frequency shift are extracted based on the two‐dimensional Fourier transform and the least squares method. When signal noise ratio = 5 dB, the accuracy rate of flapping‐wing frequency extraction can reach at 100%, the maximum relative error of frequency shift for inner contour extraction is less than 3.0%, and the maximum relative error of frequency shift for outer contour extraction is less than 7.2%. [ABSTRACT FROM AUTHOR]

In this paper, we present a tailored shooting and bouncing ray (SBR) method for the fast solution of electromagnetic (EM) scattering from a moving target. And, the micro-Doppler features of the moving target are investigated using a time-frequency analysis technique. In our method, a dynamic spatial division technique is employed to accelerate facet information processing and ray-tracing progress of the moving target. At first, the two coordinate systems are established, which are the geodetic coordinate system (GCS) and the local coordinate system (LCS). In GCS, the target is moving with translation and rotation. The dynamic spatial division is established in LCS to store the facet information and remain relatively stationary to the target. In comparison with the traditional SBR method, this technique avoids repetitive spatial division at each moment in the GCS. Then, ray tracing is performed to find the illuminated facets in the LCS. Finally, the scattering field and the phase compensation are computed in the GCS. In numerical simulations, the verification and computation efficiency comparison are provided using our method and other solutions (MLFMM, RL-GO, and traditional SBR). Moreover, the micro-Doppler features are extracted and analyzed using the time-frequency analysis technique, which includes the precession and spin of the missile, and the rotation of the aircraft. Meanwhile, the micro-Doppler spectra of the target is also compared with the theoretical Doppler of equivalent strong scattering points, which are obtained using the instantaneous high-resolution range profile (HRRP). [ABSTRACT FROM AUTHOR]

The importance of radar-based human activity recognition has increased significantly over the last two decades in safety and smart surveillance applications due to its superiority in vision-based sensing in the presence of poor environmental conditions like low illumination, increased radiative heat, occlusion, and fog. Increased public sensitivity to privacy protection and the progress of cost-effective manufacturing have led to higher acceptance and distribution of this technology. Deep learning approaches have proven that manual feature extraction that relies heavily on process knowledge can be avoided due to its hierarchical, non-descriptive nature. On the other hand, ML techniques based on manual feature extraction provide a robust, yet empirical-based approach, where the computational effort is comparatively low. This review outlines the basics of classical ML- and DL-based human activity recognition and its advances, taking the recent progress in both categories into account. For every category, state-of-the-art methods are introduced, briefly explained, and their related works summarized. A comparative study is performed to evaluate the performance and computational effort based on a benchmarking dataset to provide a common basis for the assessment of the techniques' degrees of suitability. [ABSTRACT FROM AUTHOR]

Human action recognition based on optical and infrared video data is greatly affected by the environment, and feature extraction in traditional machine learning classification methods is complex; therefore, this paper proposes a method for human action recognition using Frequency Modulated Continuous Wave (FMCW) radar based on an asymmetric convolutional residual network. First, the radar echo data are analyzed and processed to extract the micro-Doppler time domain spectrograms of different actions. Second, a strategy combining asymmetric convolution and the Mish activation function is adopted in the residual block of the ResNet18 network to address the limitations of linear and nonlinear transformations in the residual block for micro-Doppler spectrum recognition. This approach aims to enhance the network’s ability to learn features effectively. Finally, the Improved Convolutional Block Attention Module (ICBAM) is integrated into the residual block to enhance the model’s attention and comprehension of input data. The experimental results demonstrate that the proposed method achieves a high accuracy of 98.28% in action recognition and classification within complex scenes, surpassing classic deep learning approaches. Moreover, this method significantly improves the recognition accuracy for actions with similar micro-Doppler features and demonstrates excellent anti-noise recognition performance.

Compared with traditional Electromagnetic (EM) wave radars, vortex EM wave radars can simultaneously observe the micro-motion components projected onto the radar’s radial and perpendicular planes, providing more information for target recognition. The current research on the micro-Doppler effect of vortex EM wave radar is still in its infancy, and the extraction of three-dimensional micro-motion parameters of rotating targets has been preliminarily achieved. However, the impact of target translation was not considered. Therefore, the micro-Doppler effect of translational rotating targets in vortex EM wave radar is studied in this paper. The angular Doppler properties of translational rotating targets are derived, and a three-dimensional micro-motion parameter extraction method based on the 1/4 micro-motion period Doppler frequency shift curve is proposed. Estimation of parameters such as target rotation frequency, rotation radius, rotation vector and translational velocity vector is achieved. The correctness of angular Doppler properties and the effectiveness of parameter extraction method are verified by simulations.

Radar echo modeling based on dynamics and kinematics serves as the theoretical basis for micro-Doppler characteristic analysis and projectile parameter extractions. First, the initial disturbance of a projectile in the straight-line ballistic segment is analyzed. Based on the dynamic equation of the projectile, an angular motion model of the projectile characterized by two circular motion modes is established. Moreover, the motion definitions of projectile spin, nutation, and precession are explained. Subsequently, the parameterized characterization of the micro-Doppler signal produced by the angular motion of the projectile is derived. Furthermore, the mapping relationship between the angular motion of the projectile and the radar echo is obtained at the signal level. Taking high-speed spin projectile and a low-speed spin tail projectile as examples, when the angular motion of the two targets are affected by the initial disturbance, the radar echo signal model of the two targets is simulated and time-frequency analysis is carried out. The validity of the theoretical analysis and the model is verified by comparing the simulation results with the measured data of the projectile. Therefore, the micro-Doppler effect theory of projectile is enriched and verified through theoretical analysis, simulation modeling, and experimental verification. This study provides theoretical and technical support for the identification and analysis of projectile motion characteristics.

In this paper, we study the micro-motion characteristics of multi-feature targets based on a double pulse coherent system under atmospheric conditions. The theoretical model for echo signal and micro-motion characteristics of a 3D target in double pulse coherent system is deduced. We discuss the influence of micro-motion characteristics, the relative size of light spot and target, target shapes, and incident direction on frequency shift. LRCS (Lidar cross-section), echo waveform, intensity and radiation energy distribution under different conditions are obtained additionally. Simulation results conclude that these parameters are of advantage to the inversion of target shape properties and motion types.

Background: Main anatomical landmarks of retrosigmoid craniotomy are transverse sinus (TS), sigmoid sinus (SS), and the confluence of both. Anatomical references and guidance based on preoperative imaging studies are less reliable in the posterior fossa than in the supratentorial region. Simple intraoperative real-time guidance methods are in demand to increase safety. Methods: This manuscript describes the localization of TS, SS, and TS-SS junction by audio blood flow detection with a micro-Doppler system. Conclusion: This is an additional technique to increase safety during craniotomy and dura opening, widening the surgical corridor to secure margins without carrying risks nor increase surgical time. [ABSTRACT FROM AUTHOR]

With the proliferation of unmanned aerial vehicles (UAVs) in both commercial and military use, the public is paying increasing attention to UAV identification and regulation. The micro-Doppler characteristics of a UAV can reflect its structure and motion information, which provides an important reference for UAV recognition. The low flight altitude and small radar cross-section (RCS) of UAVs make the cancellation of strong ground clutter become a key problem in extracting the weak micro-Doppler signals. In this paper, a clutter suppression method based on an orthogonal matching pursuit (OMP) algorithm is proposed, which is used to process echo signals obtained by a linear frequency modulated continuous wave (LFMCW) radar. The focus of this method is on the idea of sparse representation, which establishes a complete set of environmental clutter dictionaries to effectively suppress clutter in the received echo signals of a hovering UAV. The processed signals are analyzed in the time–frequency domain. According to the flicker phenomenon of UAV rotor blades and related micro-Doppler characteristics, the feature parameters of unknown UAVs can be estimated. Compared with traditional signal processing methods, the method based on OMP algorithm shows advantages in having a low signal-to-noise ratio (−10 dB). Field experiments indicate that this approach can effectively reduce clutter power (−15 dB) and successfully extract micro-Doppler signals for identifying different UAVs. [ABSTRACT FROM AUTHOR]

To study the electromagnetic scattering of tilt-rotor aircraft during multi-mode continuous flight, a dynamic simulation approach is presented. A time-varying mesh method is established to characterize the dynamic rotation and tilting of tilt-rotor aircraft. Shooting and bouncing rays and the uniform theory of diffraction are used to calculate the multi-mode radar cross-section (RCS). And the scattering mechanisms of tilt-rotor aircraft are investigated by extracting the micro-Doppler and inverse synthetic aperture radar images. The results show that the dynamic RCS of tilt-rotor aircraft in helicopter and airplane mode exhibits obvious periodicity, and the transition mode leads to a strong specular reflection on the rotor's upper surface, which increases the RCS with a maximum increase of about 36 dB. The maximum micro-Doppler shift has functional relationships with flight time, tilt speed, and wave incident direction. By analyzing the change patterns of maximum shift, the real-time flight state and mode can be identified. There are some significant scattering sources on the body of tilt-rotor aircraft that are distributed in a planar or point-like manner, and the importance of different scattering sources varies in different flight modes. The pre-studies on the key scattering areas can provide effective help for the stealth design of the target. [ABSTRACT FROM AUTHOR]

Deep learning architectures are being increasingly adopted for human activity recognition using radar technology. A majority of these architectures are based on convolutional neural networks (CNNs) and accept radar micro-Doppler signatures as input. The state-of-the-art CNN-based models employ batch normalization (BN) to optimize network training and improve generalization. In this paper, we present whitening-aided CNN models for classifying human activities with radar sensors. We replace BN layers in a CNN model with whitening layers, which is shown to improve the model's accuracy by not only centering and scaling activations, similar to BN, but also decorrelating them. We also exploit the rotational freedom afforded by whitening matrices to align the whitened activations in the latent space with the corresponding activity classes. Using real data measurements of six different activities, we show that whitening provides superior performance over BN in terms of classification accuracy for a CNN-based classifier. This demonstrates the potential of whitening-aided CNN models to provide enhanced human activity recognition with radar sensors. [ABSTRACT FROM AUTHOR]

Spectrum aliasing occurs in signal echoes when the sampling frequency does not comply with the Nyquist Sampling Theorem. In this scenario, the extraction of micro-motion parameters becomes challenging. This paper proposes a spectral de-aliasing method for micro-motion signals based on a complex-valued U-Net network. Zero interpolation is employed to insert zeros into the echo, effectively increasing the sampling frequency. After zero interpolation, the micro-motion signal contains both real micro-motion signal frequency components and new frequency components. Short-Time Fourier Transform (STFT) is then applied to transform the zero-interpolated echo from the time domain to the time–frequency domain. Furthermore, a complex-valued U-Net training model is utilized to eliminate redundant frequency components generated by zero interpolation, thereby achieving the frequency reconstruction of micro-motion signal echoes. Finally, the training models are employed to process the measured data. The theoretical analysis, simulations, and experimental results demonstrate that this method is robust and feasible, and is capable of addressing the problem of micro-motion signal echo spectrum aliasing in narrowband radar. [ABSTRACT FROM AUTHOR]

This paper investigates the use of micro-Doppler spectrogram signatures of flying targets, such as drones and birds, to aid in their remote classification. Using a custom-designed 10-GHz continuous wave (CW) radar system, measurements from different scenarios on a variety of targets were recorded to create datasets for image classification. Time/velocity spectrograms generated for micro-Doppler analysis of multiple drones and birds were used for target identification and movement classification using TensorFlow. Using support vector machines (SVMs), the results showed an accuracy of about 90% for drone size classification, about 96% for drone vs. bird classification, and about 85% for individual drone and bird distinction between five classes. Different characteristics of target detection were explored, including the landscape and behavior of the target.

With the continuous advancement of autonomous driving and monitoring technologies, there is increasing attention on non-intrusive target monitoring and recognition. This paper proposes an ArcFace SE-attention model-agnostic meta-learning approach (AS-MAML) by integrating attention mechanisms into residual networks for pedestrian gait recognition using frequency-modulated continuous-wave (FMCW) millimeter-wave radar through meta-learning. We enhance the feature extraction capability of the base network using channel attention mechanisms and integrate the additive angular margin loss function (ArcFace loss) into the inner loop of MAML to constrain inner loop optimization and improve radar discrimination. Then, this network is used to classify small-sample micro-Doppler images obtained from millimeter-wave radar as the data source for pose recognition. Experimental tests were conducted on pose estimation and image classification tasks. The results demonstrate significant detection and recognition performance, with an accuracy of 94.5%, accompanied by a 95% confidence interval. Additionally, on the open-source dataset DIAT-μRadHAR, which is specially processed to increase classification difficulty, the network achieves a classification accuracy of 85.9%.

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.

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Polarization and micro-Doppler signatures are widely used for target detection and recognition in harsh sensing environments. However, accurately estimating polarization scattering matrices and Doppler frequencies of scattering centers (SCs) remains challenging. This letter incorporates the multichannel spectrum interpolation (MSI) with the CLEAN algorithm. The proposed algorithm can estimate the Doppler frequencies and scattering matrices of target SC from fully polarized data with high precision. Compared with the traditional detection-after-imaging workflow, the proposed method takes an iterative suppression technique, which improves the estimation accuracy and reduces detection time. The effectiveness of the proposed method is verified based on the simulation and actual radar data. The estimation accuracy measured by polarized similarity is 99.9%, and the normalized root-mean-squared error (RMSE) of the estimated Doppler frequency is 4.8% in simulation. [ABSTRACT FROM AUTHOR]

Human body detection is very important in the research of automotive safety technology. The extraction and analysis of human micro-motion based on frequency-modulated continuous wave (FMCW) radar is gradually receiving attention. Aimed at the modulation effect of human micro-motion on FMCW radar, a human running model is proposed to study human radar characteristics. According to the scattering characteristics of rigid bodies, the analytical expression of human running radar echoes is established. By using time–frequency analysis, the micro-Doppler features in the radar echoes are extracted during the running period. Under running conditions, the micro-Doppler characteristics of key components are studied. This model is applied to the real FMCW radar verification platform, and the runners are measured at a distance of 10 m. The fit rate of all parts of the human body can reach above 90%. The overall fit rate of the human model can reach up to 90.6%. The model proposed is a realistic and simple human kinematic model. This model, which can realize the real simulation of a running human body and provide strong support for human target radar echo analysis, can fill the deficiency of FMCW radar technology in the complex motion model. [ABSTRACT FROM AUTHOR]

Millimetre-wave frequency-modulated continuous-wave (FMCW) radar is widely used in various scenarios. However, it is often affected by static and dynamic clutter interference, which has a negative impact on its performance. Specifically, these clutter signals are often mistaken for target signals, leading to false detection and affecting the accuracy of target tracking and localization. In addition, dynamic clutter sources, such as other moving objects, also bring about Doppler frequency shift interference, further affecting the measurement of target velocity. In this paper, addressing the issue of static clutter, we propose a frame mean subtraction method. Additionally, for the more complex problem of dynamic clutter, we introduce a filtering approach guided by distance-Doppler information. This method utilizes a mask generated in real-time by tracking the temporal distance information of the target as prior information for filtering radar signals. Subsequently, we employ a novel fractional short-time Fourier transform to extract the Doppler feature spectrogram of the radar signal. Finally, a ResNet-50 model trained on the Doppler spectrograms of interference-free radar signals is used to test the Doppler maps generated from the filtered radar signals. After testing, the classification accuracy reaches 97.5%. This result shows that the micro-Doppler spectrum obtained by filtering the radar signal collected in complex scenes using the proposed method is highly similar to the micro-Doppler spectrum of the target to be measured. In addition, the proposed filtering method not only plays the role of signal filtering, but also enhances the strength of the target signal and provides more detailed information for the subsequent recognition task.

In recent years, radar-based human activity recognition has attracted the interest of a large number of researchers. Many researchers have proposed various effective processing algorithms. However, a good data processing algorithm not only has high recognition accuracy but also should be closer to the real application environment, such as having better detection robustness and detection sensitivity. This paper proposes a residual-bi-LSTM-attention hybrid multi-network, which has high recognition accuracy and the advantages of strong robustness and detection sensitivity. First, we collected data on five different activities of 13 volunteers in a laboratory setting. Then, after processing the collected data through the proposed algorithm, the micro-Doppler characteristics of each action are obtained. Finally, these desired features are fed into the proposed network for classification and recognition. Experimental results confirm the efficiency and accuracy of the proposed algorithm.

When wireless communication signals impinge on a moving human they are affected by micro-Doppler. A passive receiver of the resulting signals can calculate the spectrogram that produces different signatures depending on the human activity. This constitutes a significant privacy breach when the human is unaware of it. This paper presents a methodology for preventing this when we want to do so by injecting into the transmitted signal frequency variations that obfuscate the micro-Doppler signature. We assume a system that uses orthogonal frequency division multiplexing (OFDM) and a passive receiver that estimates the spectrogram based on the instantaneous channel state information (CSI). We analyze the impact of our approach on the received signal and we propose two strategies that do not affect the demodulation of the digital communication signal at the intended receiver. To evaluate the performance of our approach we use an IEEE 802.11-based OFDM system and realistic human signal reflection models.

The detection and recognition of marine targets can be improved by utilizing the micro-motion induced by ocean waves. However, distinguishing and tracking overlapping targets is challenging when multiple extended targets overlap in the range dimension of the radar echo. In this paper, we propose a multi-pulse delay conjugate multiplication and layered tracking (MDCM-LT) algorithm for micro-motion trajectory tracking. The MDCM method is first applied to obtain the conjugate phase from the radar echo, which enables high-precision micro-motion extraction and overlapping state identification of extended targets. Then, the LT algorithm is proposed to track the sparse scattering points belonging to different extended targets. In our simulation, the root mean square errors of the distance and velocity trajectories were better than 0.277 m and 0.016 m / s , respectively. Our results demonstrate that the proposed method has the potential to improve the precision and reliability of marine target detection through radar. [ABSTRACT FROM AUTHOR]

In this paper, we present a tailored shooting and bouncing ray (SBR) method for the fast solution of electromagnetic (EM) scattering from a moving target. And, the micro-Doppler features of the moving target are investigated using a time-frequency analysis technique. In our method, a dynamic spatial division technique is employed to accelerate facet information processing and ray-tracing progress of the moving target. At first, the two coordinate systems are established, which are the geodetic coordinate system (GCS) and the local coordinate system (LCS). In GCS, the target is moving with translation and rotation. The dynamic spatial division is established in LCS to store the facet information and remain relatively stationary to the target. In comparison with the traditional SBR method, this technique avoids repetitive spatial division at each moment in the GCS. Then, ray tracing is performed to find the illuminated facets in the LCS. Finally, the scattering field and the phase compensation are computed in the GCS. In numerical simulations, the verification and computation efficiency comparison are provided using our method and other solutions (MLFMM, RL-GO, and traditional SBR). Moreover, the micro-Doppler features are extracted and analyzed using the time-frequency analysis technique, which includes the precession and spin of the missile, and the rotation of the aircraft. Meanwhile, the micro-Doppler spectra of the target is also compared with the theoretical Doppler of equivalent strong scattering points, which are obtained using the instantaneous high-resolution range profile (HRRP).

The extraction of the micro-Doppler (m-D) feature based on time-frequency distribution (TFD) is of great significance for target detection and identification. To improve the feature extraction performance, numerous TFDs have been developed, with the majority falling under Cohen’s class. Nevertheless, these TFDs basically face a trade-off between artifact suppression and energy concentration. The main reason is that each Cohen’s class TFD is constructed by applying the two-dimensional Fourier transform to a kerneled ambiguity function directly, while existing kernels generally attenuate artifacts at the expense of losing valuable information. In this paper, a TFD reconstruction method employing an adaptive short-time kernel (ASTK) is developed in the framework of sparse representation (SR) theory to overcome this trade-off and enhance the m-D feature. Firstly, the task of the optimal kernel is explained from the viewpoint of the instantaneous auto-correlation function (IAF). Secondly, based on the quasi-linear frequency modulation feature of most m-D signals during short-time periods, the distribution rule of the short-time IAF (STIAF) in the ambiguity plane is concluded. Guided by this rule, an ASTK that can effectively remove unwanted artifacts with the least information loss is designed. Finally, an SR-based reconstruction procedure is conducted on the kerneled STIAF to generate an artifact-free TFD with high energy concentration, which can effectively enhance the m-D feature. Experiments using both simulated and real-world m-D signals demonstrate the effectiveness of the proposed method.

The boundaries of tracking and sensing solutions are continuously being pushed. A stimulation in this field over recent years is exploiting the properties of millimeter wave (mmWave) radar to achieve simultaneous tracking and sensing of multiple objects. This paper aims to provide a critical analysis of the current literature surrounding multi-object tracking and sensing with short-range mmWave radar. There is significant literature available regarding single-object tracking using mmWave radar, demonstrating the maturity of single-object tracking systems. However, innovative research and advancements are also needed in the field of mmWave radar multi-object tracking, specifically with respect to uniquely identifying multiple target tracks across an interrupted field of view. In this article, we aim to provide an overview of the latest progress in multi-target tracking. In particular, an attempt to phrase the problem space is made by firstly defining a typical multi-object tracking architecture. We then highlight the areas for potential advancements. These areas include sensor fusion, micro-Doppler feature analysis, specialized and generalized activity recognition, gait, tagging and shape profile. Potential multi-object tracking advancements are reviewed and compared with respect to adaptability, performance, accuracy and specificity. Although the majority of the literature reviewed has a focus on human targets, most of the methodologies can be applied to targets consisting of different profiles and characteristics to that of humans. Lastly, future research directions are also discussed to shed light on research opportunities and potential approaches in the open research areas. [ABSTRACT FROM AUTHOR]

The growing number of civil applications in which Unmanned Aerial Vehicles (UAVs) are involved can create many concerns for airspace security and surveillance. Gathering as much information as possible about a drone can be crucial to apply proper countermeasures if a potentially dangerous situation is detected. Of course, the presence of a UAV can be detected by radar, but it is possible to extend the system capabilities to obtain additional information. For example, in the case in which the UAV is equipped with propellers, the radar-measured rotational speed could be important information to classify the type of UAV or to reveal if it is carrying some possibly harmful payload. In addition, the rotational speed measured through radar could be used for different purposes, such as to detect a drone manumission, to estimate its maximum payload, or for predictive maintenance of the drone. Measuring the propellers' rotational speed with radar systems is a critical task, as the Doppler generated by the rotation can be very high, and it is very difficult to find commercial radar systems in the market able to handle such a high Doppler. Another problem is caused by the typically very small Radar Cross-Section (RCS) of the propellers, which makes their detection even more difficult. In the literature, common detection techniques are based on the measurement of the Doppler effect produced by the propellers to derive their rotational speed, but due to the very limited capabilities of commercial sensors, this approach can be applied only at very low values of the rotational speed. In this work, a different approach based on a Frequency-Modulated Continuous Wave (FMCW) radar is proposed, which exploits the vibration of the UAV generated by the rotation of the propellers. The phenomenon and how the sensor can detect it will be presented, which is joined with a performance analysis comparing different estimation techniques for the indirect measurement of the propellers' speed to evaluate the potential benefits of the proposed approach. [ABSTRACT FROM AUTHOR]

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We studied the use of a millimeter-wave frequency-modulated continuous wave radar for gait analysis in a real-life environment, with a focus on the measurement of the step time. A method was developed for the successful extraction of gait patterns for different test cases. The quantitative investigation carried out in a lab corridor showed the excellent reliability of the proposed method for the step time measurement, with an average accuracy of 96%. In addition, a comparison test between the millimeter-wave radar and a continuous-wave radar working at 2.45 GHz was performed, and the results suggest that the millimeter-wave radar is more capable of capturing instantaneous gait features, which enables the timely detection of small gait changes appearing at the early stage of cognitive disorders. [ABSTRACT FROM AUTHOR]

Aiming at improving the method of underwater target recognition, this paper proposes a method to estimate the micro-Doppler spectrum parameters of propeller by using inverse Radon transform, so as to obtain the characteristic parameters of propeller. This paper firstly analyzes the basic concepts of micro-Doppler and inverse Radon transform, then defines the algorithm steps of inverse Radon transform for propeller parameter estimation and carries out simulation. The simulation results show that this method can effectively estimate the propeller rotational speed, blade number, blade length and initial spatial position. [ABSTRACT FROM AUTHOR]