micro-Doppler Signatures Research Articles

Development of RF-Photonic System for Automatic Targets’ Nonlinear Rotational/Flapping/Gliding Signatures Imaging Applications

Nowadays, developing the radio frequency (RF)-photonic sensors suitable for several Defence and civil applications is emerging due to its various unique advantages. In today’s scientific world, the involvement of low radar cross-section (RCS) airspace targets, such as unman aerial vehicles, drones, mini-helicopters, ornithopters, bionic birds, etc., is more for the purpose of airspace traffic management/guidance, materials delivery, legal/illegal surveillance and Defence/offense applications. The detection and recognition of such targets using the conventional RF sensors (which process only the main Doppler) is almost impossible. Therefore, extracting the distinctive micro-Doppler (m-D) signatures of these low-RCS targets and using them to image/differentiate/recognize their postures become significant which is the main contribution in this paper. A C-band continuous wave RF-photonic sensor is developed and different low-RCS targets: 2/3 blades rotating propeller system (drones), cone like structure (warhead) and a bionic-bird (spy-bird); are operated in front of it. The different nonlinear postures, of these targets, covered in our experimental measurements are m-D extraction of a slowly moving propeller system, detection of back-and-forth movements of a rotational propeller system, discerning the moving and static rotational propeller systems, simultaneous extraction of m-D signatures of a rotational and moving targets and recognition of flapping/gliding motions of bionic-bird. The analysis and recognition of all these postures using the experimentally generated m-D signatures are described.

LDA 기반 분산 레이다 시스템을 활용한 손동작 분류 알고리즘 개발 및 성능 분석

In this study, we propose a linear discriminant analysis (LDA)-based classification algorithm for distributed radars that share their data at a data fusion center over wireless channels. Instead of sharing high-dimensional micro-Doppler signature (MDS) data over the resource-limited backhaul, each radar applies the LDA algorithm to reduce the dimension of the data. Because the dimension-reduced MDS data is a vector with complex values, it is expressed as a real-valued probability vector using the softmax processing method to reduce the quantization error. A probability vector with a sum of 1 is transmitted to the fusion center via a pyramid vector quantization (PVQ) scheme. To improve the classification performance at the fusion center, bit resources were adaptively allocated based on the separability of the distributed radars. The proposed algorithm was assessed using MNIST(modified national institute of standards and technology database) data as well as hand gesture data measured using a USRP(universal software radio peripheral)-based test bed.

Numerical and Experimental Studies on the Micro-Doppler Signatures of Freely Flying Insects at W-Band

Remote sensing techniques in the microwave frequency range have been successfully used in the context of bird, bat and insect measurements. This article breaks new ground in the analysis of freely flying insects by using a continuous-wave (CW) radar system in W-band, i.e., higher mm-wave frequencies, by measuring and analyzing the micro-Doppler signature of their wing beat motion. In addition to numerical and experimental methods, the investigation also includes the development of a new signal processing method using a cepstrogram approach in order to automatically determine the wing beat frequency. In this study, mosquitoes (culex pipiens) and bees (apis mellifera) are considered as model insects throughout the measurement campaign. It was found that 50 independent micro-Doppler measurements of mosquitoes and bees can be clearly distinguished from each other. Moreover, the proposed radar signal model accurately matches the experimental measurements for both species.

Clustering-Driven DGS-Based Micro-Doppler Feature Extraction for Automatic Dynamic Hand Gesture Recognition.

We propose in this work a dynamic group sparsity (DGS) based time-frequency feature extraction method for dynamic hand gesture recognition (HGR) using millimeter-wave radar sensors. Micro-Doppler signatures of hand gestures show both sparse and structured characteristics in time-frequency domain, but previous study only focus on sparsity. We firstly introduce the structured prior when modeling the micro-Doppler signatures in this work to further enhance the features of hand gestures. The time-frequency distributions of dynamic hand gestures are first modeled using a dynamic group sparse model. A DGS-Subspace Pursuit (DGS-SP) algorithm is then utilized to extract the corresponding features. Finally, the support vector machine (SVM) classifier is employed to realize the dynamic HGR based on the extracted group sparse micro-Doppler features. The experiment shows that the proposed method achieved 3.3% recognition accuracy improvement over the sparsity-based method and has a better recognition accuracy than CNN based method in small dataset.

Vision Transformer with Convolutional Encoder–Decoder for Hand Gesture Recognition using 24-GHz Doppler Radar

Transformers combined with convolutional encoders have been recently used for hand gesture recognition (HGR) using micro-Doppler signatures. In this letter, we propose a vision-transformer-based architecture for HGR with multiantenna continuous-wave Doppler radar receivers. The proposed architecture consists of three modules: 1) a convolutional encoder–decoder, 2) an attention module with three transformer layers, and 3) a multilayer perceptron. The novel convolutional decoder helps to feed patches with larger sizes to the attention module for improved feature extraction. Experimental results obtained with a dataset corresponding to a two-antenna continuous-wave Doppler radar receiver operating at 24 GHz (published by Skaria et al.) confirm that the proposed architecture achieves an accuracy of 98.3% which substantially surpasses the state-of-the-art on the used dataset.

Detection of Micro-Doppler Signals of Drones Using Radar Systems with Different Radar Dwell Times

Not any radar dwell time of a drone radar is suitable for detecting micro-Doppler (or jet engine modulation, JEM) produced by the rotating blades in radar signals of drones. Theoretically, any X-band drone radar system should detect micro-Doppler of blades because of the micro-Doppler effect and partial resonance effect. Yet, we analyzed radar data detected by three radar systems with different radar dwell times but similar frequency and velocity resolution, including Radar−α, Radar−β, and Radar−γ with radar dwell times of 2.7 ms, 20 ms, and 89 ms, respectively. The results indicate that Radar−β is the best radar for detecting micro-Doppler (i.e., JEM signals) produced by the rotating blades of a quadrotor drone, DJI Phantom 4, because the detection probability of JEM signals is almost 100%, with approximately 2 peaks, whose magnitudes are similar to that of the body Doppler. In contrast, Radar−α can barely detect any micro-Doppler, and Radar−γ detects weak micro-Doppler signals, whose magnitude is only 10% of the body Doppler’s. Proper radar dwell time is the key to micro-Doppler detection. This research provides an idea for designing a cognitive micro-Doppler radar by changing radar dwell time for detecting and tracking micro-Doppler signals of drones.

Unsupervised Domain Adaptation for Disguised-Gait-Based Person Identification on Micro-Doppler Signatures

In recent years, gait-based person identification has gained significant interest for a variety of applications, including security systems and public security forensics. Meanwhile, this task is faced with the challenge of disguised gaits. When a human subject changes what he or she is wearing or carrying, it becomes challenging to reliably identify the subject’s identity using gait data. In this paper, we propose an unsupervised domain adaptation (UDA) model, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Guided Subspace Alignment under the Class-aware</i> condition (G-SAC), to recognize human subjects based on their disguised gait data by fully exploiting the intrinsic information in gait biometrics. To accomplish this, we employ neighbourhood component analysis (NCA) to create an intrinsic feature subspace from which we can obtain similarities between normal and disguised gaits. With the aid of a proposed constraint for adaptive class-aware alignment, the class-level discriminative feature representation can be learned guided by this subspace. Our experimental results on a measured micro-Doppler radar dataset demonstrate the effectiveness of our approach. The comparison results with several state-of-the-art methods indicate that our work provides a promising domain adaptation solution for the concerned problem, even in cases where the disguised pattern differs significantly from the normal gaits. Additionally, we extend our approach to more complex multi-target domain adaptation (MTDA) challenge and video-based gait recognition tasks, the superior results demonstrate that the proposed model has a great deal of potential for tackling increasingly difficult problems.

Dual-Scale Doppler Attention for Human Identification.

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

Dual-Biometric Human Identification Using Radar Deep Transfer Learning.

Accurate human identification using radar has a variety of potential applications, such as surveillance, access control and security checkpoints. Nevertheless, radar-based human identification has been limited to a few motion-based biometrics that are solely reliant on micro-Doppler signatures. This paper proposes for the first time the use of combined radar-based heart sound and gait signals as biometrics for human identification. The proposed methodology starts by converting the extracted biometric signatures collected from 18 subjects to images, and then an image augmentation technique is applied and the deep transfer learning is used to classify each subject. A validation accuracy of 58.7% and 96% is reported for the heart sound and gait biometrics, respectively. Next, the identification results of the two biometrics are combined using the joint probability mass function (PMF) method to report a 98% identification accuracy. To the best of our knowledge, this is the highest reported in the literature to date. Lastly, the trained networks are tested in an actual scenario while being used in an office access control platform to identify different human subjects. We report an accuracy of 76.25%.

RCS Estimation of Drone Motion Using Mesh-Element Rotation in MoM and Micro-Doppler Signatures

For an automatic recognition system, a huge number of micro-Doppler signature images is required to train a classifier. However, in general, measurements are only possible for hovering drones, so the dynamic radar cross section (RCS) of a drone with various motions is estimated through EM simulation and converted into a micro-Doppler signature image. In this work, we use far-field approximation and mesh-element rotation in the method of moments to quickly estimate the dynamic RCS of a drone in various motions. First, 3-D meshes and the impedance matrix of a single propeller are generated only once. Instead of rotating the propeller, the position information of the mesh elements is rotated without recreating 3-D meshes or the impedance matrix. Next, using the mirror-image symmetric characteristics of drone propellers and the far-field approximation, the dynamic RCS of multiple propellers is synthesized from that of a single propeller. Finally, the dynamic RCS was estimated at 3 and 9 GHz and converted to micro-Doppler signatures, spectrogram, and cadence-velocity diagram (CVD). As a result, it was more accurate to estimate the dynamic RCS at 9 GHz, and it was easier to obtain the rotation frequency at 3 GHz in the CVD.

A Multi-Rotor Drone Micro-Motion Parameter Estimation Method Based on CVMD and SVD

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.

A novel feature extraction method of human‐vehicle classification based on low resolution radar

In this paper, a novel method using target movement characteristic is proposed to classify human and vehicle based on low resolution radar system. Many studies have demonstrated the ability of radar micro-Doppler signature for ground moving targets classification. However, the micro-Doppler signals are not always observed in practical application. Considering the motion characteristic of human and vehicle, a multi-frame joint scheme is proposed, in which three suitable features are extracted and the Random Forest classifier is used to identify three kinds of moving targets. The experiment results with satisfied classification accuracy prove that the proposed method is capable of human-vehicle classification.

Angle-Insensitive Human Motion and Posture Recognition Based on 4D Imaging Radar and Deep Learning Classifiers

The need for technologies for Human Activity Recognition (HAR) in home environments is becoming more and more urgent because of the aging population worldwide. Radar-based HAR is typically using micro-Doppler signatures as one of the main data representations, in conjunction with classification algorithms often inspired from deep learning methods. One of the limitations of this approach is the challenging classification of movements at unfavorable aspect angles (i.e., close to 90°) and of static postures in between continuous sequences of activities. To address this problem, a hierarchical processing and classification pipeline is proposed to fully exploit all the information available from millimeter-wave (mm-wave) 4D imaging radars, specifically the azimuth and elevation information in conjunction to the more conventional range, Doppler, received power, and time features. The proposed pipeline uses the two complementary data representations of Point Cloud (PC) and spectrogram, and its performance is validated using an experimental dataset with 6 activities performed by 8 participants. The results show good performance of the proposed pipeline compared with alternative baseline approaches in the literature, and the effect of key parameters such as the amount of training data, signal-to-noise levels, and virtual aperture size is investigated. Leave-one-subject-out test is also applied to study the impact of body characteristics on the generalizability of the trained classifiers.

Dop-DenseNet: Densely Convolutional Neural Network-Based Gesture Recognition Using a Micro-Doppler Radar

Hand gesture recognition is an efficient and practical solution for the non-contact human–machine interaction in smart devices. To date, vision-based methods are widely used in this research area, but they are susceptible to light conditions. To address this issue, radar-based gesture recognition using micro-Doppler signatures can be applied as an alternative. Accordingly, the use of a novel densely convolutional neural network model, Dop-DenseNet, is proposed in this paper for improving hand gesture recognition in terms of classification accuracy and latency. The model was designed with cross or skip connections in a dense architecture so that the former features, which can be lost in the forward-propagation process, can be reused. We evaluated our model with different numbers of filter channels and experimented with it using the Dop-Net dataset, with different time lengths of input data. As a result, it was found that the model with 64 3 × 3 filters and 200 time bins of micro-Doppler spectrogram data could achieve the best performance trade-off, with 99.87% classification accuracy and 3.1 ms latency. In comparison, our model remarkably outperformed the selected state-of-the-art neural networks (GoogLeNet, Res- Net-50, NasNet-Mobile, and MobileNet-V2) using the same Dop-Net dataset.

Automatic recognition of sonar targets using feature selection in micro-Doppler signature

Currently, the use of intelligent systems for the automatic recognition of targets in the fields of defence and military has increased significantly. The primary advantage of these systems is that they do not need human participation in target recognition processes. This paper uses the particle swarm optimization (PSO) algorithm to select the optimal features in the micro-Doppler signature of sonar targets. The micro-Doppler effect is referred to amplitude/phase modulation on the received signal by rotating parts of a target such as propellers. Since different targets' geometric and physical properties are not the same, their micro-Doppler signature is different. This Inconsistency can be considered a practical issue (especially in the frequency domain) for sonar target recognition. Despite using 128-point fast Fourier transform (FFT) for the feature extraction step, not all extracted features contain helpful information. As a result, PSO selects the most optimum and valuable features. To evaluate the micro-Doppler signature of sonar targets and the effect of feature selection on sonar target recognition, the simplest and most popular machine learning algorithm, k-nearest neighbor (k-NN), is used, which is called k-PSO in this paper because of the use of PSO for feature selection. The parameters measured are the correct recognition rate, reliability rate, and processing time. The simulation results show that k-PSO achieved a 100% correct recognition rate and reliability rate at 19.35 s when using simulated data at a 15 dB signal-to-noise ratio (SNR) angle of 40°. Also, for the experimental dataset obtained from the cavitation tunnel, the correct recognition rate is 98.26%, and the reliability rate is 99.69% at 18.46s. Therefore, the k-PSO has an encouraging performance in automatically recognizing sonar targets when using experimental datasets and for real-world use.

A strategy for evaluating micro-doppler signature and motion parameter of ship over time-varying sea surface

ABSTRACT The micro-Doppler (m-D) signature can describe the motion state and structure of the target in detail. It can be considered as an effective method for ship target detection and recognition in the marine environment. In this paper, an effective strategy is proposed to simulate the m-D signature and extract the ship’s motion parameters on the time-varying sea surface. For this purpose, a ship target micro-motion model based on six degrees of freedom is first established. Then, the public domain strip (PDSTRIP) method is used to realize real-time estimation of ship motion. Next, a high-precision electromagnetic scattering model is used to generate radar echoes of a ship on the rough sea. Finally, the m-D signature can be acquired and analysed based on the obtained radar echo. The numerical results show that the instantaneous frequency and motion parameters can be estimated well from the m-D signature. At the same time, the proposed model can also be used to calibrate the Inverse Synthetic Aperture Radar (ISAR) of ship targets in the marine environment.

Gait-Based Person Identification and Intruder Detection Using mm-Wave Sensing in Multi-Person Scenario

Using mm-wave sensing to identify persons by the way they walk has recently emerged as a promising solution of biometrics, and has found versatile applications in security surveillance, automatic access control, and health monitoring. In this paper, we build a system named MGait for indoor multi-person identification and intruder detection based on gait micro-Doppler (m-D) signatures captured by a low-cost mm-wave radar. In multi-person scenario where multiple subjects concurrently share and walk within the same physical space, the proposed system continuously detects and separately tracks each subject in the range-Doppler (R-D) space frame by frame to extract their respective gait m-D signatures. Then, an open-set identification network is trained by a large-margin Gaussian mixture (L-GM) loss to learn highly discriminative feature representations and compel the learned features of the training set to follow a Gaussian mixture distribution, with each component representing a registered user. As such, the known users can be directly identified based on the class-posterior probability and the intruder can also be rejected by setting a probability threshold. The proposed system is verified on real radar measurements collected by a 77 GHz FMCW radar, achieving an overall accuracy of 88.59&#x0025; in identifying up to 5 subjects, including 1 known user and 4 intruders, freely and concurrently moving in a corridor space.

Drones Classification by the Use of a Multifunctional Radar and Micro-Doppler Analysis

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.

Extraction of Micro-Doppler Feature Using LMD Algorithm Combined Supplement Feature for UAVs and Birds Classification

In the past few decades, the demand for reliable and robust systems capable of monitoring unmanned aerial vehicles (UAVs) increased significantly due to the security threats from its wide applications. During UAVs surveillance, birds are a typical confuser target. Therefore, discriminating UAVs from birds is critical for successful non-cooperative UAVs surveillance. Micro-Doppler signature (m-DS) reflects the scattering characteristics of micro-motion targets and has been utilized for many radar automatic target recognition (RATR) tasks. In this paper, the authors deploy local mean decomposition (LMD) to separate the m-DS of the micro-motion parts from the body returns of the UAVs and birds. After the separation, rotating parts will be obtained without the interference of the body components, and the m-DS features can also be revealed more clearly, which is conducive to feature extraction. What is more, there are some problems in using m-DS only for target classification. Firstly, extracting only m-DS features makes incomplete use of information in the spectrogram. Secondly, m-DS can be observed only for metal rotor UAVs, or large UAVs when they are closer to the radar. Lastly, m-DS cannot be observed when the size of the birds is small, or when it is gliding. The authors thus propose an algorithm for RATR of UAVs and interfering targets under a new system of L band staring radar. In this algorithm, to make full use of the information in the spectrogram and supplement the information in exceptional situations, m-DS, movement, and energy aggregation features of the target are extracted from the spectrogram. On the benchmark dataset, the proposed algorithm demonstrates a better performance than the state-of-the-art algorithms. More specifically, the equal error rate (EER) proposed is 2.56% lower than the existing methods, which demonstrates the effectiveness of the proposed algorithm.

Person Identification With Low Training Sample Based on Micro-Doppler Signatures Separation

Due to the ability to handle low-light environments, poor weather conditions and privacy protection, person identification based on radar micro-Doppler (m-D) signatures has emerged as a research interest. Moreover, as it is always difficult to construct large-scale radar data sets, we propose an approach to recognize a person’s identity with limited training sample. In which, the fine m-D signal processing technology is first used to obtain high-quality m-D data spectrograms. A three-layer convolutional principal component analysis network with a dimension optimization architecture (CPCAN-3) is then designed to learn the highly discriminative features and address the identification problem. The model has few network parameters, is easy to train, and has low dependence on a large number of training samples. Different daily activities data are captured in an indoor environment to evaluate the performance of proposed method, and the state-of-the-art algorithms are chosen for comparison. The experimental results show that the proposed scheme performs better performance than the others on small data sets. Especially when the motion of “running” is adopted to identify persons, the model achieves 98% accuracy on the identification of ten people.