micro-Doppler Signatures Research Articles

Low-SNR Recognition of UAV-to-Ground Targets Based on Micro-Doppler Signatures Using Deep Convolutional Denoising Encoders and Deep Residual Learning

The rapid development of flight control technology has made unmanned aerial vehicles (UAVs) widely used in high-precision strikes on the battlefield. The premise of this is to achieve accurate target recognition using UAV-based radars. Aiming at three typical ground targets, including pedestrians, wheeled vehicles, and tracked vehicles, the micro-Doppler modulation caused by the random vibration of the UAV is analyzed in this article for the first time. To improve the recognition accuracy under low signal-to-noise ratios (SNRs), Doppler signals are transformed into time–frequency images, and a deep convolutional denoising encoder (DCDE) is designed to effectively remove the noise without suppressing micro-Doppler characteristics. To avoid the complicated micro-Doppler feature extraction, deep residual learning that can reduce the burden of network training and gain higher learning efficiency compared with traditional deep convolutional neural networks (DCNNs) is adopted. Recognition results under various occasions using denoised micro-Doppler images and designed residual learning network indicate that the proposed method has higher precision and better robustness than current methods. Even when the SNR is only −16 dB, the overall recognition accuracy still exceeds 90%.

FMNet: Latent Feature-Wise Mapping Network for Cleaning Up Noisy Micro-Doppler Spectrogram

Micro-Doppler signatures contain considerable information about target dynamics. However, the radar sensing systems are easily affected by noisy surroundings, resulting in uninterpretable motion patterns on the micro-Doppler spectrogram. Meanwhile, radar returns often suffer from multipath, clutter and interference. These issues lead to difficulty in, for example motion feature extraction, activity classification using micro Doppler signatures ($\mu$-DS), etc. In this paper, we propose a latent feature-wise mapping strategy, called Feature Mapping Network (FMNet), to transform measured spectrograms so that they more closely resemble the output from a simulation under the same conditions. Based on measured spectrogram and the matched simulated data, our framework contains three parts: an Encoder which is used to extract latent representations/features, a Decoder outputs reconstructed spectrogram according to the latent features, and a Discriminator minimizes the distance of latent features of measured and simulated data. We demonstrate the FMNet with six activities data and two experimental scenarios, and final results show strong enhanced patterns and can keep actual motion information to the greatest extent. On the other hand, we also propose a novel idea which trains a classifier with only simulated data and predicts new measured samples after cleaning them up with the FMNet. From final classification results, we can see significant improvements.

Verification of calculation method for drone micro-Doppler signature estimation

Drones micro-Doppler signatures obtained by FMCW radars are an excellent procedure for malicious drone detection, identification and classification. There are a number of contributions dealing with recorded spectrograms with these micro-Doppler signatures, but very low number of them has analyzed possibility to calculate echo caused by drone moving parts. In this paper, starting from already existing mathematical apparatus, we presented such spectrograms as a function of changing drone moving parts characteristics: rotor number, blades number, blade length and rotor moving speed. This development is the part of a wider project intended to prevent malicious drone usage.

TFR Recovery From Incomplete Micro-Doppler Signal via AL-ADMM-Net

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.

Decomposition for Multi-Component Micro-Doppler Signal With Incomplete Data

When echoes of micromotion targets are overlapping in the time-frequency (TF) domain and sampling data are missing, the decomposition of the multicomponent micro-Doppler (m-D) signals is challenging. To address this issue, this letter proposes a method for multicomponent m-D signal decomposition by iterations of the instantaneous frequencies (IFs), individual components, and complex envelopes. To initialize the IFs, the well-focused time-frequency representation (TFR) is obtained by sparse reconstruction of the incomplete data, and then the IFs of the TFR can be estimated by the short-time variational mode decomposition (STVMD) algorithm. After initialization, the IFs, individual components, and complex envelopes are updated by the intrinsic chirp component decomposition (ICCD), alternating direction method (ADMM) of multipliers, and least-square-error criterion (LSEC), respectively. Finally, the proposed method is verified by simulation and application to real data.

Feature Extraction Method of Helicopter Target Based on Flicker Phenomenon Combined With Phase Compensation

For the typical multi-component periodic micro-Doppler signal parameter estimation, a helicopter rotor feature extraction method based on flicker phenomenon and phase compensation is proposed in this paper. The method firstly analyzes the formation mechanism of helicopter rotor flicker phenomenon in time domain and time-frequency domain through the integral model. Then the demodulation operator of the micro-Doppler signal is constructed for the phase compensation of the echoes with respect to the time-frequency flicker characteristics and the phase information of the slow time dimension of the target echoes. The parameter combination of the possible number of blades and rotational speed is predicted using the time domain flicker interval, and the phase compensation process is evaluated and optimized to significantly reduce the amount of computation. Finally, the parameter estimation of rotor blade number, blade length and rotational speed is acquired by calculating whether the center frequency of each flicker after phase compensation is periodically focused to zero frequency. The simulation results show that the method can effectively estimate the main parameters of helicopter rotor blades, and the processing results of the measured data show that the proposed method has more stable performance compared with OMP algorithm and Hough transform, which provides a new technical way for helicopter rotor blade feature extraction.

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

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

Range-Max Enhanced Ultrawideband Micro-Doppler Signatures of Behind-the-Wall Indoor Human Motions

In this article, an ultrawideband (UWB) radar is first employed to probe through the opaque wall media to detect behind-the-wall human motions. By employing such a radar, a high-resolution time-range map with different body parts’ reflections highly discriminable in range direction can be obtained. Second, a high-pass filter is applied to remove the wall effects in the raw time-range map. Then, with the aim of exploiting the rich range information so as to enhance their corresponding micro-Doppler features, a novel range-max enhancement strategy is proposed to extract the most significant micro-Doppler feature of each time–frequency cell along range direction for a specific motion. Finally, the effectiveness of the proposed motion feature enhancement strategy is investigated by means of onsite experiments. Comparative classifications using different convolutional neural network (CNN) structures show that the proposed approach outperforms other state-of-the-art micro-Doppler feature extraction methods. The comparison with the narrowband detection case also proves its superiority in feature enhancement in the narrowband detection scene.

Multiscenario Open-Set Gait Recognition Based on Radar Micro-Doppler Signatures

Unlike face recognition, gait recognition does not require the cooperation of human targets, making it an attractive research area in both academia and industry. While there has been extensive research in recent years on the feasibility of employing radar micro-Doppler signatures for gait recognition, little research has been conducted on how to deal with unknown target detection in micro-Doppler-based gait recognition problems, referred to as ’open-set’ gait recognition. In this case, it is probable that the unknown samples will resemble known samples, hence increasing the likelihood of misclassification. In this paper, we focus on the problem of multi-scenario open-set gait recognition and present a new open-set classifier that minimizes misclassification in open-set recognition. We create a compact representation space to reduce false negatives with a supervised contrastive learning method and propose an ensemble-based out-of-distribution (OOD) detection module so that false positives can be alleviated at both the pixel and semantic levels. We perform the radar measurements in eight different walking manners and construct a micro-Doppler-based multi-scenario gait dataset for further evaluate the proposed method’s performance. The results reveal that the proposed method outperforms the previous state-of-the-art open-set models, demonstrating its great potential for solving the radar-based multi-scenario open-set gait recognition problem. This research would facilitate gait recognition in complex scenarios and make non-contact sensing applicable to industrial security.

Analysis and Simulation of the Micro-Doppler Signature of a Ship With a Rotating Shipborne Radar at Different Observation Angles

Differences in the motion of different parts of a target cause the echo signal to contain specific Doppler modulation information, i.e., the micro-Doppler (m-D) effect. This phenomenon provides an effective way to detect targets in marine environments. In this study, based on the establishment of the micromotion model of a rotating surveillance radar and analysis of the m-D frequency, the geometrical optics and physical optics (GO-PO) method and the time-frequency analysis technique are used to obtain the radar cross section (RCS) and m-D signature of a ship with a shipborne radar at different observation angles. The ship, as the main component of the echo, is associated with the main energy. Finding the optimum angle to observe the shipborne radar is of great importance. The results show that the m-D signatures of the shipborne radar are not clear when the elevation angle is greater than 60° but are clear when the elevation angle is less than 55°. Moreover, some motion parameters can be extracted from the m-D signature, such as the period of the ship micromotion. The rotation speed of the shipborne radar can be obtained and is consistent with the set speed. This can help identify and track the key parts of a ship with local motion.

SISO Radar-Based Human Movement Direction Determination Using Micro-Doppler Signatures

Human movement direction determination (HMDD) is a significant task in human detection and recognition applications, but it remains a challenge when utilizing single-input and single-output (SISO) radar because angle information cannot be accessed without multiple receiving antennas. Moreover, adopting multiple-output radar systems for this task would limit their applicability in a broader range of scenarios, as these systems require a larger placement area and a more extensive calibration procedure than SISO radar. Tackling this problem, this paper presents an effective method for the SISO-radar-based human movement direction determination task. Our method combines the joint time-frequency analysis (JTFA) technique with a proposed bio-inspired feature extraction process, thereby producing an accurate perception of moving direction based on the analysis of micro-Doppler signatures. The radar simulation and measurements are separately conducted and used to establish the corresponding dataset, so the superior performance of our method could be verified on the HMDD task. Furthermore, this article delves into why existing criteria for evaluating an HMDD model’s performance are insufficient in multi-direction situations, followed by an introduction of “small error concentration" and “omnidirectional error uniformity", as well as their evaluation protocols, to describe and measure the bias of an HMDD model’s results on multi-direction determination problems. By comparing with existing both traditional and deep-learning methods, we confirm our method’s superior in the HMDD task, and we believe that our research will aid in the advancement of human detection and recognition applications using SISO radar.

Semisupervised Human Activity Recognition With Radar Micro-Doppler Signatures

Human activity recognition (HAR) plays a vital role in many applications, such as surveillance, in-home monitoring, and health care. Portable radar sensor has been increasingly used in HAR systems in combination with deep learning (DL). However, it is both difficult and time-consuming to obtain a large-scale radar dataset with reliable labels. Insufficient labeled data often limit the generalization of DL models. As a result, the performance of DL models will drop when being applied to a new scenario. In this sense, only labeling a small portion of data in the large-scale radar dataset is more feasible. In this article, we propose a semisupervised transfer learning (TL) algorithm, “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">joint domain and semantic transfer learning</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">JDS-TL</i> ),” for radar-based HAR, which is composed of two modules: unsupervised domain adaptation (DA) and supervised semantic transfer. By employing a sparsely labeled dataset to train the HAR model, the proposed method alleviates the need of labeling a significantly large number of radar signals. We adopt a public radar micro-Doppler spectrogram dataset including six human activities to evaluate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">JDS-TL</i> . Experiments show that the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">JDS-TL</i> is able to recognize the six activities with an average accuracy of 87.6% when there are only 10% instances labeled in the training dataset. Ablation analysis also demonstrates the efficiency of the DA and the semantic transfer modules.

Through-the-Wall Micro-Doppler De-Wiring Technique via Cycle-Consistent Adversarial Network

The radar penetrating technique has aroused a keen interest in the research community, due to its superior abilities for through-the-wall indoor human motion monitoring. Micro-Doppler signatures in this situation play a significant role in recognition and classification for human activities. However, the live wire buried in the wall introduces additive clutters to the spectrograms. Such degraded spectrograms drastically affect the performance of behind-the-wall human activity detection. In this paper, an ultra-wideband (UWB) radar system is utilized in the through-the-wall scenario to get the feature enhanced micro-Doppler signature called range-max time-frequency representation (R-max TFR). Then, a recently introduced Cycle-Consistent Generative Adversarial Network (Cycle GAN) is employed to realize the end-to-end de-wiring task. Cycle GAN can learn the mapping between spectrograms with and without the live wire effect. To minimize the wiring clutters, a loss function called identity loss is introduced in this work. Finally, the proposed de-wiring approach is evaluated through classification. The results show that the proposed Cycle GAN architecture outperforms other state-of-art de-wiring methods.

Multi-Frequency Radar Micro-Doppler Based Classification of Micro-Drone Payload Weight

The use of drones for recreational, commercial and military purposes has seen a rapid increase in recent years. The ability of counter-drone detection systems to sense whether a drone is carrying a payload is of strategic importance as this can help determine the potential threat level posed by a detected drone. This paper presents the use of micro-Doppler signatures collected using radar systems operating at three different frequency bands for the classification of carried payload of two different micro-drones performing two different motions. Use of a KNN classifier with six features extracted from micro-Doppler signatures enabled mean payload classification accuracies of 80.95, 72.50 and 86.05%, for data collected at S-band, C-band and W-band, respectively, when the drone type and motion type are unknown. The impact on classification performance of different amounts of situational information is also evaluated in this paper.

Exploiting Unique State Transitions to Capture Micro-Doppler Signatures of Human Actions Using CW Radar

Exploiting Unique State Transitions to Capture Micro-Doppler Signatures of Human Actions Using CW Radar

Star-Shaped Wheel for Mechanical Micro-Doppler Modulation

This letter investigates the possibility of using deliberated geometries for rotating targets, which may improve the detectability and classification by means of its micro-Doppler signature. Beyond the signature provided by the rotation rate of the target, every particular geometry leads to specific and unique modulation waveforms that increase the information related to this target. Different star-shaped wheels with particular geometries have been designed demonstrating different reflection characteristics and modulation waveforms. The signature of these rotating star-shaped wheels has been measured in an anechoic chamber using a general-purpose 24 GHz frequency-modulated continuous wave (FMCW) radar platform and a detection algorithm based on the derivative of the cross-correlation is proposed to validate the proposed approach.

DecompNet: Deep Context Dependent Decomposition Network for Micro-Doppler Signature of Walking Human

In recent years, human activity recognition using radar micro-Doppler signature has been the focus of many researches. Echo signal from body limbs is the origin of motion parameters estimation. To this end, decomposition of this composite signal into components corresponding to each limb is necessary and rather complex. In this paper, a novel method for body micro-Doppler signal decomposition is proposed using a deep convolutional neural network designated DecompNet which uses a context window on the spectral input stream to enhance the decomposition performance. This network is able to decompose 9 components of body limbs. The proposed network, is able to perform signal decomposition and denoising simultaneously, even in low SNR conditions. So, it outperforms the other similar methods both in terms of decomposition ability and robustness to observation noise. Simulation results show that DecompNet can outperform mathematical transform-based methods and also keep working in the SNRs as low as −15dB.

Rotor UAV's Micro-Doppler Signal Detection and Parameter Estimation Based on FRFT-FSST.

The micro-Doppler signal generated by the rotors of an Unmanned Aerial Vehicle (UAV) contains the structural features and motion information of the target, which can be used for detection and classification of the target, however, the standard STFT has the problems such as the lower time-frequency resolution and larger error in rotor parameter estimation, an FRFT (Fractional Fourier Transform)-FSST (STFT based synchrosqueezing)-based method for micro-Doppler signal detection and parameter estimation is proposed in this paper. Firstly, the FRFT is used in the proposed method to eliminate the influence of the velocity and acceleration of the target on the time-frequency features of the echo signal from the rotors. Secondly, the higher time-frequency resolution of FSST is used to extract the time-frequency features of micro-Doppler signals. Moreover, the specific solution methodologies for the selection of window length in STFT and the estimation of rotor parameters are given in the proposed method. Finally, the effectiveness and accuracy of the proposed method for target detection and rotor parameter estimation are verified through simulation and measured data.

Removing Micro-Doppler Effect in ISAR Imaging by Promoting Joint Sparsity in Range Profile Sequences and the Time-Frequency Domain

The micro-Doppler effect induced by rotating parts interferes the inverse synthetic aperture radar (ISAR) imaging of the target’s rigid body heavily. To extract useful signals from rigid body and remove the micro-Doppler interference, this paper proposes a novel approach by promoting joint sparsity of the rigid body both in range profile sequences and time-frequency domain. The problem is formulated and modeled as a convex optimization with double joint sparsity penalty, and the exact solution can be obtained after multiple alternate iterations. In addition, the paper analyzes the effect of the scatterer’s linear migration through resolution cells (MTRC) on micro-Doppler removal. In order to avoid the signals from migrated rigid scatterers being removed together with the micro-Doppler signals, this paper proposes another improved reweighted method, in which the weights are designed to correlate slices with same frequency in neighboring range cells, to preserve migrated rigid scatterers and remove micro-Doppler interference meanwhile. Experimental results based on both synthetic and Electromagnetic simulated data demonstrate the validity of the proposed methods.

Portable UWB RADAR Sensing System for Transforming Subtle Chest Movement Into Actionable Micro-Doppler Signatures to Extract Respiratory Rate Exploiting ResNet Algorithm

Contactless or non-invasive technology for the monitoring of anomalies in an inconspicuous and distant environment has immense significance in health-related applications, in particular COVID-19 symptoms detection, diagnosis, and monitoring. Contactless methods are crucial specifically during the COVID-19 epidemic as they require the least amount of involvement from infected individuals as well as healthcare personnel. According to recent medical research studies regarding coronavirus, individuals infected with novel COVID-19-Delta variant undergo elevated respiratory rates due to extensive infection in the lungs. This appalling situation demands constant real-time monitoring of respiratory patterns, which can help in avoiding any pernicious circumstances. In this paper, an Ultra-Wideband RADAR sensor enquote XeThru X4M200 is exploited to capture vital respiratory patterns. In the low and high frequency band, X4M200 operates within the 6.0-8.5 GHz and 7.25-10.20 GHz band, respectively. The experimentation is conducted on six distinct individuals to replicate a realistic scenario of irregular respiratory rates. The data is obtained in the form of spectrograms by carrying out normal (eupnea) and abnormal (tachypnea) respiratory. The collected spectrogram data is trained, validated, and tested using a cutting-edge deep learning technique called Residual Neural Network or ResNet. The trained ResNet model’s performance is assessed using the confusion matrix, precision, recall, F1-score, and classification accuracy. The unordinary skip connection process of the deep ResNet algorithm significantly reduces the underfitting and overfitting problem, resulting in a classification accuracy rate of up to 90%.