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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.

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.

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.

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.

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.

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 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.

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.

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.

The micro-motion target echoes can be regarded as the accumulation of a few strong scattering points echoes, which are naturally sparse. Therefore, the compressed sensing (CS) reconstruction method can be used to analyze the incomplete micro-Doppler signal and extract the micro-motion features. Traditional CS reconstruction algorithms are time-consuming and sensitive to the selection of model parameters, limiting the performance of micro-motion feature extraction. This paper proposes a deep unfolded method to achieve the reconstruction of the time-frequency representation (TFR) of incomplete micro-Doppler signals. First, the joint time-frequency (JTF) transform is used to construct the CS model and the Training data is obtained according to the model. Then, the alternating direction method of multipliers (ADMM) algorithm is constructed into an iterative network named ADMM-net corresponding to the iterative solution process. An auxiliary loss is designed into the ADMM-net called AL-ADMM-net to improve the quality of reconstruction. The AL-ADMM-net is trained by the training data to learn the optimal parameters. Furthermore, an AL-ADMM-net iterative method is proposed when the micro-Doppler signal has phase corruption. Simulations are given to prove the effectiveness of the proposed method.

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.

Developments in sensor fusion systems led to extensive research in autonomous vehicles, and object detection is a crucial aspect of vehicle operation. Detecting obstacles can be difficult due to the wide range of potential obstructions, the characteristics of each sensor, and the influence of the surrounding environment. In this paper, the authors use automotive radar data and various neural networks to classify various objects (vehicles, single and multiple people, and bicycles). Combined with the vehicle radar, this research proposed a rapid radar algorithmic implementation for locating, monitoring and extracting micro-Doppler. The authors evaluate three distinct neural network architectures for the five recorded classes of targets: the basic CNN, the residual network, and the combined neural architecture of convolution and recurrent layers. Considerable accuracy is 95.6% immediately before identification of the radar spectrogram from (~0.55 s to produce 0.5 s long spectrogram).

Micro-Doppler effects of moving vehicles in a radar system are mainly induced by the rotation of wheels, whose features are closely related to the numbers, positions and radiuses of wheels. These parameters of wheels are critical for the vehicle classification and recognition. However, most micro-Doppler features extraction works of vehicles are unable to explicitly extract parameters of wheels. In this paper, a parameter extraction method of vehicle wheels using micro-Doppler features based on the matching pursuit (MP) is proposed. The micro-Doppler signals of wheels are generally weak comparing to redundant echo signals induced by other irrelevant parts of the vehicle, which makes the micro-Doppler features difficult to extract. In this case, several signal atom sets are created according to the motion states of irrelevant parts of vehicle and MP is performed to suppress the redundant signals. After the suppression, micro-Doppler signals induced by wheels have become the major part of the echo signal. Another atom set is generated according to the rotational motion of wheels to perform MP again. Then the wheel parameters, such as the estimated numbers, positions and radiuses, are extracted. Simulation results demonstrate that the proposed method is feasible in feature extraction of moving vehicle. Besides, the accuracy can be guaranteed when the signal-to-noise ratio is greater than –5 dB.

The vortex ElectroMagnetic (EM) wave has a unique spiral phase wavefront. The rotational Doppler effect, induced by the lateral micromotion of a target is expected to provide a new mode of radar target detection. Under the illumination of vortex EM waves, the micromotion of a cone-shaped target periodically modulates the instantaneous frequency of the radar echo; this can effectively reflect the geometric characteristics and micromotion parameters of a cone-shaped target. This paper focuses on the estimation of cone-shaped target parameters under forward-looking radar conditions. First, based on the principle of vortex EM wave target rotational Doppler detection, a mathematical equation describing the vortex EM wave of a cone-shaped target echo is derived, and an echo rotational Doppler model is established. Second, a method for cone-shaped target parameter estimation under forward-looking conditions is proposed. Using the two-dimensional rotational Doppler information of the scattering points at the top and bottom of a cone-shaped target, the micromotion and geometric parameters of the target can be effectively estimated. The simulation results verify the effectiveness and robustness of the method proposed in this paper.

Micro-Doppler signatures from the rotating propellers of a drone can be utilized to distinguish the drone from clutter or airborne organisms with similar radar cross section (RCS) levels, such as birds and bats. To obtain the micro-Doppler signatures of a drone, calculation or measurement of the electric field scattered from the rotating propellers is essential. In this paper, using the relative angle concept and far-field approximation, we propose a way to rapidly estimate the dynamic RCS of a drone with several propellers according to its movement. In addition, based on the fact that the shape of the propeller does not change even if it rotates, we construct an impedance matrix only once and apply the matrix to the method of moments instead of the iterative process of calculating the impedance matrix and inverse matrix for each rotation angle of the propeller. Finally, by using the Fourier transform of the results from the proposed method, the rotation frequencies of the propellers according to the movement of the drone can be obtained.

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.

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.

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.

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.

In this study, we examine the use of micro-Doppler signals produced by different blades (i.e., puller and lifting blades) to aid in radar-based target recognition of small drones. We categorize small drones into three types based on their blade types: fixed-wing drones with only puller blades, multi-rotor drones with only lifting blades, and hybrid vertical take-off and landing (VTOL) fixed-wing drones with both lifting and puller blades. We quantify the radar signatures of the three drones using statistical measures, such as signal-to-noise ratio (SNR), signal-to-clutter ratio (SCR), Doppler speed, Doppler frequency difference (DFD), and Doppler magnitude ratio (DMR). Our findings show that the micro-Doppler signals of lifting blades in all three drone types were stronger than those of puller blades. Specifically, the DFD and DMR values of pusher blades were below 100 Hz and 0.3, respectively, which were much smaller than the 200 Hz and 0.8 values for lifting blades. The micro-Doppler signals of the puller blades were weaker and more stable than those of the lifting blades. Our study demonstrates the potential of using micro-Doppler signatures modulated by different blades for improving drone detection and the identification of drone types by drone detection radar.

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.

Radars are expected to become the main sensors in various civilian applications, ranging from health-care monitoring to autonomous driving. Their success is mainly due to the availability of both low cost integrated devices, equipped with compact antenna arrays, and computationally efficient signal processing techniques. An increasingly important role in the field of radar signal processing is played by machine learning and deep learning techniques. Their use has been first taken into consideration in human gesture and motion recognition, and in various healthcare applications. More recently, their exploitation in object detection and localization has been also investigated. The research work accomplished in these areas has raised various technical problems that need to be carefully addressed before adopting the above mentioned techniques in real world radar systems. In this manuscript, a comprehensive overview of the machine learning and deep learning techniques currently being considered for their use in radar systems is provided. Moreover, some relevant open problems and current trends in this research area are analysed. Finally, various numerical results, based on both synthetically generated and experimental datasets, and referring to two different applications are illustrated. These allow readers to assess the efficacy of specific methods and to compare them in terms of accuracy and computational effort.

Detection and classification of vulnerable road users (VRUs) such as pedestrians and cyclists is a key requirement for the realization of fully autonomous vehicles. Radar-based classification of VRUs can be achieved by exploiting differences in the micro-Doppler signatures associated with VRUs. Specifically, machine learning (ML) algorithms can be trained to classify VRUs using the spectral content of radar signals. The performance of these models depends on the quality and quantity of the data used during the training process. Currently, data collection is typically done through measurements or low fidelity physics, primitive-based simulations. The feasibility of carrying out measurements to collect training data is typically limited by the vast amounts of data required and practicality issues when using VRUs like animals. In this paper, we present a computationally efficient, high fidelity physics-based simulation workflow that can be used to obtain a large quantity of spectrograms from the micro-Doppler signatures of VRUs. The simulations are conducted on full-scale VRU models with a 77 GHz, frequency-modulated continuous-wave (FMCW) radar sensor model. Here, we collect the spectrograms of 4 targets; car, pedestrian, cyclist and dog at different speeds and angles-of-arrival. This data is then used to train a 5-layer convolutional neural network (CNN) that achieves nearly 100% classification accuracy after 5 epochs. Studies are conducted to investigate the impact of training data size, velocity and observation time window size on the accuracy of the CNN. Results from this study demonstrate how an accuracy of 95% can be realized using spectrograms obtained over a 0.2 s time window.

The capability of a radar system to detect and identify the low-slow-small rotor unmanned aerial vehicle (UAV) is intensely important for management and control in low altitude, and it can be enhanced by the characteristics of UAV, which inherently carries micro-Doppler information. In this paper, we establish a micro-motion model of rotor UAV, and analyze the range walking and Doppler broadening caused by micro-motion. Hence, based on the characteristics of micro-Doppler of the rotors apart from the main body for the moving UAV, a micro-motion target detection and micro-Doppler parameters estimation method is proposed. Firstly, a method based on datashift in range dimension is used to compensate the translational component of the main body. Secondly, for the echo after translation compensation, a parameter estimation method of micro-Doppler based on the optimal demodulation operator is proposed. The operator contents the maximum likelihood criterion, which strengthens the difference between the micro-Doppler signal and the background clutters, so that more signal energy can be accumulated while the clutter is suppressed. Subsequently, the micro-Doppler parameters of the micro-motion component are obtained by the optimal demodulator, so that the detection and identification of the target are realized. Additionally, the performance of proposed method, including parameter estimation, signal-to-noise ratio (SNR) improvement and the calculation efficiency is analyzed. Finally, the simulation and experimental results reveal that the proposed method does possess the capability to improve the performance of detection and recognition of rotor UAV.

Eight different driver head movements are measured using a millimeter-wave FMCW radar mounted in the dashboard of a car. The micro-Doppler signatures are converted into a spectrogram image format for analysis and classification purposes. The eight different head motions exhibit unique time-frequency profiles, which can be classified by deep learning algorithms. In this study, a convolutional neural network is used to classify the eight head motions with an optimized window size. Various dataset permutations are considered, such as the effect of window width on classification accuracy and the classification accuracy of head motions in a still car compared to a moving car.

This study shows an approach for classifying road users using a 24-GHz millimeter-wave radar. The sensor transmits multiple linear frequency–modulated waves, which enable range estimation and Doppler-shift estimation of targets in the scene. We aimed to develop a solution for localization and classification, which yielded the same performance when the sensor was fixed on ground or mounted on a moving platform such as a car or quadcopter. In this proposed approach, classification was achieved using supervised learning and a set of hand crafted features independent of relative speed between the target and sensor. The proposed model is based on obtaining micro-Doppler information; only one receiver is used. Therefore, in addition to the target reflectivity, no geometrical information is used. For our study, we selected three classes: pedestrians, cyclists, and cars. We then illustrated distinctive micro-Doppler features for each class based on simulations, which we compared with real-world data. Our results confirm that a limited set of low-complexity features yields high accuracy scores when the target’s trajectory does not excessively deviate from the radar’s radial direction.

The aim of this study was to solve the problem that the existing identification parameters of rotor unmanned aerial vehicles (UAVs) are few and limited by the detection mode, and an identification method for estimating the rotor blade width based on the peak time-shift effect is proposed for the first time. Taking the width of the rotor blade as the parameter to identify the rotor of UAVs, the time-shift effect and its relationship with rotor blade width are verified by theoretical analysis and simulation. The proposed time-shift method has the characteristics of high-precision extraction of rotor width, and its effectiveness is verified by simulation and experiments. The aspect ratio of the rotor is accurately extracted based on the proposed time-shift method under the condition of an unknown pitch angle. Simulation results show that the estimation accuracy of the width and aspect ratio is up to 98 and 98.4%, respectively. The experimental results show that the relative errors of the width and aspect ratio are less than 7 and 4%, respectively. This study provides the theoretical basis and technical support for the high-accuracy identification of rotorcraft UAVs.

Flying birds and Unmanned Aerial Vehicles (UAVs) are typical “low, slow, and small” targets with low observability. The need for effective monitoring and identification of these two targets has become urgent and must be solved to ensure the safety of air routes and urban areas. There are many types of flying birds and UAVs that are characterized by low flying heights, strong maneuverability, small radar cross-sectional areas, and complicated detection environments, which are posing great challenges in target detection worldwide. “Visible (high detection ability) and clear-cut (high recognition probability)” methods and technologies must be developed that can finely describe and recognize UAVs, flying birds, and “low-slow-small” targets. This paper reviews the recent progress in research on detection and recognition technologies for rotor UAVs and flying birds in complex scenes and discusses effective detection and recognition methods for the detection of birds and drones, including echo modeling and recognition of fretting characteristics, the enhancement and extraction of maneuvering features in ubiquitous observation mode, distributed multi-view features fusion, differences in motion trajectories, and intelligent classification via deep learning. Lastly, the problems of existing research approaches are summarized, and we consider the future development prospects of target detection and recognition technologies for flying birds and UAVs in complex scenarios.

We propose an efficient method to estimate various parameters of a helicopter blade including the number Bnum and the length by separating the blade signal using independent component analysis (ICA) and an analytic solution obtained by monostatic/bistatic geometry. The proposed method is composed of two parts, one for even Bnum and one for odd Bnum. For even Bnum the blade parameters are estimated using ICA plus analytic estimation. For odd Bnum the parameters are estimated using two ICAs plus analytic estimation. Experimental results using the radar signal of a blade model calculated by physical optics demonstrate that the proposed method successfully separates the blade signals for both even and odd Bnums and estimates parameters with high accuracy.

Envision a ubiquitously device-free motion sensing, this work focuses on the analysis of Wi-Fi-based hand gesture trajectory tracking by utilizing the Doppler frequency obtained from channel state information (CSI). Because human limb movement generates variant micro-Doppler signatures produced by the contribution of different parts of the hand and arm surfaces to the spectrum, an estimation technique is proposed to extract the temporal profile of the hand-only Doppler signature. With a set of Doppler profiles from different pairs of Wi-Fi antennas, hand trajectory can be traced by exploiting the multi-static Doppler radar model. The Kalman filter (KF) was applied to mitigate the accumulated noise from the recursive process of trajectory estimation. To validate the proposed method, a human limb model was developed to simulate the deterministic movement of a hand gesture by exploiting the non-rigid motion of the robotic arm. The electromagnetic wave scattered from the human limb model was computed using a physical optics (PO) approximation to simulate time-variant CSI. In the experiment, the hand Doppler signature could be successfully extracted from the spectrum with an error of less than 4 Hz at the 90th percentile of the CDF. With the extracted profiles, the trajectories of a square and M-shaped gesture were successfully traced, albeit with moderate trajectory offset of 10-20°. Measurement conducted in a meeting room with commodity Wi-Fi devices installed on laptops also confirmed the tracking capability of the proposed framework.

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.

Deep-learning-based gait classification of young and elderly adults using micro-Doppler radar (MDR) is presented in this paper. The MDR signal data were accurately simulated using an open motion-capture gait dataset, and deep-learning classification of the time-velocity distribution (i.e., spectrogram) images calculated with the generated data are presented. Utilizing a simulation, we also investigated the body parts deemed most efficient for classification based on their generation of good MDR data. As a result, the classification rate using whole-body data was 74%. However, this classification rate of using only leg data showed an accuracy of 91%, which indicates that the thighs and shanks are efficient target body parts for the gait classification of both young and elderly adults.

The Doppler effect in radio systems has been widely explored by the radio communication community. However, these studies have been limited to simple motion such as linear translation. This paper presents a model for the Doppler modulation effect, i.e., the effect of complex movement on the received signal, using a geometrical approach. Particularly, we focused on studying micro-Doppler in radio communications produced by vibrations. Exploiting this phenomenon would allow the performance of passive micro-Doppler effect sensing based on communication. In this paper, we also propose signal processing techniques to detect the presence of the micro-Doppler effect and to estimate its parameters. Then, we present some experiments which highlight the micro-Doppler effect in a radio communication context. Finally, the end of the paper discusses some potential applications that exploit this phenomenon.

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.

Ballistic target recognition is of great significance for space attack and defense. The micro-motion features, which contain spatial and motion information, can be regarded as the foundation of the recognition of ballistic targets. To take full advantage of the micro-motion information of ballistic targets, this paper proposes a method based on feature fusion to recognize ballistic targets. The proposed method takes two types of data as input: the time–range (TR) map and the time–frequency (TF) spectrum. An improved feature extraction module based on 1D convolution and time self-attention is applied first to extract the multi-level features at each time instant and the global temporal information. Then, to efficiently fuse the features extracted from the TR map and TF spectrum, deep generalized canonical correlation analysis with center loss (DGCCA-CL) is proposed to transform the extracted features into a hidden space. The proposed DGCCA-CL possesses better performance in two aspects: small intra-class distance and compact representation, which is crucial to the fusion of multi-modality data. At last, the attention mechanism-based classifier which can adaptively focus on the important features is employed to give the target types. Experiment results show that the proposed method outperforms other network-based recognition methods.

The fine state of targets can be represented by the extracted micro-Doppler (m-D) components from the radar echo. However, current methods do not consider the specialty of the m-D components, and their performance with non-sinusoidal components is poor. In this paper, a neural network is applied to signal extraction for the first time. Inspired by the semantic line detection in computer vision, the extraction of the m-D components is transformed into the network-based time–frequency curves detection problem. Specifically, a novel dual-branch network-based m-D components extraction method is proposed. According to the property of intersected multiple m-D components, the dual-branch network consisting of a continuous m-D components extraction branch, and a crossing point detection branch is designed to obtain components and cross points at the same time. In addition, a shuffle attention-fast Fourier convolution (SA-FFC) module is proposed to fuse local and global contexts and focus on key features. To solve the error correlation problem of multi-component signals, the first-order parametric continuous condition and cubic spline interpolation are employed to obtain complete and smooth components curves. Simulation and measurement results show that this method of good robustness is a good candidate for separating the non-sinusoidal m-D components with intersections.

Abstract This paper reports the investigation on the micromotion characteristics of chaff dipoles and a chaff cloud. Micromotion models of chaff dipoles and steady-state chaff clouds are discussed. Fourier transform method and time-frequency method are used to study the micromotion features of chaff dipoles, in the energy domain and time-frequency domain, respectively. Based on the characteristic parameters in these two domains, the simulation results show that there are micro-Doppler frequency components in the echo signals from chaff dipoles, and these frequency components vary periodically. Then, the micro-Doppler frequencies of chaff cloud are obtained based on the method of multipoint synthesis. Finally, the experimental results of chaff dipoles dispersion verify the micromotion characteristics of chaff dipoles and chaff cloud obtained by using the empirical mode decomposition method and the short-time Fourier transform method.

The high-speed translational motion of space targets will cause the micro-Doppler to shift, tilt, and fold, which brings great difficulty to the extraction of micromotion features. Translational motion must be compensated in advance to extract the authentic characteristics of micro-Doppler curves. To solve the problem of translational motion compensation, an estimation method of translational parameters based on deep learning theory is proposed. A polynomial model describing translational motion is constructed firstly by analyzing its dynamic principle. Meanwhile, the parametric characterization of micromotion signal is deduced by taking the cone target as an example. On this basis, two regression networks that can estimate acceleration and velocity respectively from the time-frequency graph are built using transfer learning. The translational motion compensation is accomplished with high accuracy and low computation complexity. The proposed method can also achieve satisfactory results in the presence of high intensity noise and discontinuous scattering points. Finally, the effectiveness and robustness of the proposed method are validated by simulations.

The micro-movement feature is recognized as one of the practical features of space target recognition in academic circles. The separation of the micro-Doppler curve of the scattering center is the key to feature extraction and parameter estimation, which depends on the time–frequency analysis method. The existing techniques have low separation accuracy and adaptability when there are overlap and noise in the time–frequency domain. This paper proposes a micro-Doppler feature extraction algorithm of a space target based on the modified synchro-reassigning transform (MSRT) and ridge segment linking. The MSRT can eliminate repeated assignment problems, has more accurate micro-Doppler frequency estimates than the synchro-reassigning transform, and has lower computational complexity than second-order synchronous compression and synchronous extraction transforms. The re-linking of the ridge realizes the correct connection of the micro-Doppler curves of each scattering center. The simulation data and the electromagnetic calculation data verify the method’s effectiveness.

It is of great significance to detect drones in airspace due to the substantial increase and regrettable misuse in the consumer market. In this paper, we establish a micro-motion theoretical model of a drone and analyze the micro-Doppler signature of rotor targets and the flicker mechanisms of the multi-rotor targets. Hence, for the target recognition problem of multi-rotor drones, a multi-rotor target micro-Doppler parameter estimation method is proposed. Firstly, a signal frequency domain segmentation method is proposed based on the complex variational mode decomposition (CVMD) to separate the high-frequency part of the high-frequency flicker in the frequency domain. Secondly, for the signal after frequency domain segmentation, a flicker time domain position method based on singular value decomposition (SVD) is proposed. Finally, by integrating CVMD frequency domain segmentation and SVD time domain positioning, the reconstruction of multi-rotor target scintillation at different speeds is realized, and the micro-motion parameters of rotor blades are successfully estimated. The simulation results show that the method has high accuracy in estimating the micro-motion parameters of a multi-rotor, which makes up for the shortage of the traditional method in estimating the micro-motion parameters of the multi-rotor target.

The classification of targets by the use of radars has received great interest in recent years, in particular in defence and military applications, in which the development of sensor systems that are able to identify and classify threatening targets is a mandatory requirement. In the specific case of drones, several classification techniques have already been proposed and, up to now, the most effective technique was considered to be micro-Doppler analysis used in conjunction with machine learning tools. The micro-Doppler signatures of targets are usually represented in the form of the spectrogram, that is a time–frequency diagram that is obtained by performing a short-time Fourier transform (STFT) on the radar return signal. Moreover, frequently it is possible to extract useful information that can also be used in the classification task from the spectrogram of a target. The main aim of the paper is comparing different ways to exploit the drone’s micro-Doppler analysis on different stages of a multifunctional radar. Three different classification approaches are compared: classic spectrogram-based classification; spectrum-based classification in which the received signal from the target is picked up after the moving target detector (MTD); and features-based classification, in which the received signal from the target undergoes the detection step after the MTD, after which discriminating features are extracted and used as input to the classifier. To compare the three approaches, a theoretical model for the radar return signal of different types of drone and aerial target is developed, validated by comparison with real recorded data, and used to simulate the targets. Results show that the third approach (features-based) not only has better performance than the others but also is the one that requires less modification and less processing power in a modern multifunctional radar because it reuses most of the processing facility already present.