micro-Doppler Features Research Articles

Lightweight Multi-Domain Fusion Model for Through-Wall Human Activity Recognition Using IR-UWB Radar

Impulse radio ultra-wideband (IR-UWB) radar, operating in the low-frequency band, can penetrate walls and utilize its high range resolution to recognize different human activities. Complex deep neural networks have demonstrated significant performance advantages in classifying radar spectrograms of various actions, but at the cost of a substantial computational overhead. In response, this paper proposes a lightweight model named TG2-CAFNet. First, clutter suppression and time–frequency analysis are used to obtain range–time and micro-Doppler feature maps of human activities. Then, leveraging GhostV2 convolution, a lightweight feature extraction module, TG2, suitable for radar spectrograms is constructed. Using a parallel structure, the features of the two spectrograms are extracted separately. Finally, to further explore the correlation between the two spectrograms and enhance the feature representation capabilities, an improved nonlinear fusion method called coordinate attention fusion (CAF) is proposed based on attention feature fusion (AFF). This method extends the adaptive weighting fusion of AFF to a spatial distribution, effectively capturing the subtle spatial relationships between the two radar spectrograms. Experiments showed that the proposed method achieved a high degree of model lightweightness, while also achieving a recognition accuracy of 99.1%.

Isolated point clutter suppression method for airborne STAP radar in wind farm environment

With the large-scale construction of wind farms, wind turbine isolated point clutter has an increasingly serious impact on airborne radar target detection performance. Traditional space-time adaptive processing methods cannot suppress wind turbine clutter (WTC) with spectrum broadening characteristics, which may lead to a decrease in target detection probability and an increase in false alarm rate. In this paper, a WTC suppression method for airborne radar based on micro-Doppler features is proposed, and we construct the feature subspace of wind turbine echo to distinguish wind turbine, target, clutter, and noise. First, the Sobel operator is used to process the radar range-Doppler spectrum, and the range cells of the wind turbines are preliminarily judged. Then the smallest of constant false alarm rate (SOCFAR) method is used to further confirm the range cells where the wind turbines are located. Next, Mahalanobis distance is used to estimate the optimal dictionary atomic parameters of WTC, and the updated dictionary atoms are used to construct an orthogonal projection matrix to suppress WTC. Finally, short-range nonstationary clutter and sidelobe clutter are suppressed by space-time adaptive segment processing. On the one hand, the proposed method realizes the accurate positioning of wind turbines through image edge detection and constant false alarm detection. On the other hand, Mahalanobis distance is used to estimate the atomic parameters of the wind turbine dictionary, which ensures the homogeneity of wind turbine samples after clutter suppression. The simulation and measured data results show that the proposed method can significantly reduce the false alarm rate caused by WTC while ensuring the effective detection of the target.

Extraction of Micro-Doppler Features from Unmanned Aerial Vehicles Based on Electromagnetic Simulation

Extraction of Micro-Doppler Features from Unmanned Aerial Vehicles Based on Electromagnetic Simulation

SRCNN: Stacked-Residual Convolutional Neural Network for Improving Human Activity Classification Based on Micro-Doppler Signatures of FMCW Radar

Current methods for daily human activity classification primarily rely on optical images from cameras or wearable sensors. Despite their high detection reliability, camera-based approaches suffer from several drawbacks, such as low-light conditions, limited range, and privacy concerns. To address these limitations, this article proposes the use of a frequency-modulated continuous wave radar sensor for activity recognition. A stacked-residual convolutional neural network (SRCNN) is introduced to classify daily human activities based on the micro-Doppler features of returned radar signals. The model employs a two-layer stacked-residual structure to reuse former features, thereby improving the classification accuracy. The model is fine-tuned with different hyperparameters to find a trade-off between classification accuracy and inference time. Evaluations are conducted through training and testing on both simulated and measured datasets. As a result, the SRCNN model with six stacked-residual blocks and 64 filters achieves the best performance, with accuracies exceeding 95% and 99% at 0 dB and 10 dB, respectively. Remarkably, the proposed model outperforms several state-of-the-art CNN models in terms of classification accuracy and execution time on the same datasets.

FMCW Radar Human Action Recognition Based on Asymmetric Convolutional Residual Blocks.

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.

Research on the Motion Features Model for Underwater Targets with Multiple Highlights and Multiple Micro-Motion Forms

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

Extracting Micro-Doppler Features from Multi-Rotor Unmanned Aerial Vehicles Using Time-Frequency Rotation Domain Concentration

This study addresses the growing concern over the impact of small unmanned aerial vehicles (UAVs), particularly rotor UAVs, on air traffic order and public safety. We propose a novel method for micro-Doppler feature extraction in multi-rotor UAVs within the time-frequency transform domain. Utilizing competitive learning particle swarm optimization (CLPSO), our approach divides population dynamics into three subgroups, each employing unique optimization mechanisms to enhance local search capabilities. This method overcomes limitations in traditional Particle Swarm Optimization (PSO) algorithms, specifically in achieving global optimal solutions. Our simulation and experimental results demonstrate the method’s efficiency and accuracy in extracting micro-Doppler features of rotary-wing UAVs. This advancement not only facilitates UAV detection and identification but also significantly contributes to the fields of UAV monitoring and airspace security.

Enhanced Micro-Doppler Feature Extraction Using Adaptive Short-Time Kernel-Based Sparse Time-Frequency Distribution

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.

A Lightweight Multiscale Neural Network for Indoor Human Activity Recognition Based on Macro and Micro-Doppler Features

A Lightweight Multiscale Neural Network for Indoor Human Activity Recognition Based on Macro and Micro-Doppler Features

Fast Solution of Scattering and Micro-Doppler Features from Moving Target Using a Tailored Shooting and Bouncing Ray Method

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).

Comparative Analysis of Audio Processing Techniques on Doppler Radar Signature of Human Walking Motion Using CNN Models.

Artificial intelligence (AI) radar technology offers several advantages over other technologies, including low cost, privacy assurance, high accuracy, and environmental resilience. One challenge faced by AI radar technology is the high cost of equipment and the lack of radar datasets for deep-learning model training. Moreover, conventional radar signal processing methods have the obstacles of poor resolution or complex computation. Therefore, this paper discusses an innovative approach in the integration of radar technology and machine learning for effective surveillance systems that can surpass the aforementioned limitations. This approach is detailed into three steps: signal acquisition, signal processing, and feature-based classification. A hardware prototype of the signal acquisition circuitry was designed for a Continuous Wave (CW) K-24 GHz frequency band radar sensor. The collected radar motion data was categorized into non-human motion, human walking, and human walking without arm swing. Three signal processing techniques, namely short-time Fourier transform (STFT), mel spectrogram, and mel frequency cepstral coefficients (MFCCs), were employed. The latter two are typically used for audio processing, but in this study, they were proposed to obtain micro-Doppler spectrograms for all motion data. The obtained micro-Doppler spectrograms were then fed to a simplified 2D convolutional neural networks (CNNs) architecture for feature extraction and classification. Additionally, artificial neural networks (ANNs) and 1D CNN models were implemented for comparative analysis on various aspects. The experimental results demonstrated that the 2D CNN model trained on the MFCC feature outperformed the other two methods. The accuracy rate of the object classification models trained on micro-Doppler features was 97.93%, indicating the effectiveness of the proposed approach.

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

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

Classification and Recognition Method of Non-Cooperative Object Based on Transfer Learning

Classifying and identifying non-cooperative targets plays an important role in space missions in the present age. A micro-Doppler characteristic signal is an effective means to identify the target. In order to better extract the micro-Doppler features of the target in the case of very little experimental data, a pose recognition network based on the theory of transfer learning is proposed. The source domain dataset is constructed of the time–frequency spectrums obtained by short-time Fourier transform of the simulated echo signals. The time–frequency spectrums from experiment are made as target domain. Pre-training and domain adaptation is applied in this work. Finally, the test results demonstrate that the classification accuracy of our pose recognition network based on transfer learning is improved due to the good use of simulation data and the small amount of experimental data.

Doppler Radar-Based Human Speech Recognition Using Mobile Vision Transformer

As one of the important vital features of the human body, the acquisition of a speech signal plays an important role in human–computer interaction. In this study, voice sounds are gathered and identified using Doppler radar. The skin on the neck vibrates when a person speaks, which causes the vocal cords to vibrate as well. The vibration signal received by the radar will produce a unique micro-Doppler signal according to words with different pronunciations. Following the conversion of these signals into micro-Doppler feature maps, these speech signal maps are categorized and identified. The speech recognition method used in this paper is on neural networks. CNN convolutional neural networks have a lower generalization and accuracy when there are insufficient training samples and sample extraction bias, and the training model is not suitable for use on mobile terminals. MobileViT is a lightweight transformers-based model that can be used for image classification tasks. MobileViT uses a lightweight attention mechanism to extract features with a faster inference speed and smaller model size while ensuring a higher accuracy. Our proposed method does not require large-scale data collection, which is beneficial for different users. In addition, the learning speed is relatively fast, with an accuracy of 99.5%.

A New Model for Human Running Micro-Doppler FMCW Radar Features

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.

Recognition of Approximate Motions of Human Based on Micro-Doppler Features

With the aging society approaching, the daily safety of the elderly has attracted more and more people’s attention, especially the elderly who live alone without anyone to take care of them. How to judge whether there is danger from the daily behavior of those who live alone and providing timely assistance is a research topic of health protection in the field of smart home care. Modern radar sensing technology and intelligent perception technology adopted in the domain of health care have started to gain significant interest, which can replace wearable sensors or cameras without feeling uncomfortable or having privacy issues. In this paper, some preliminary results were presented to develop an indoor monitoring system for human activity recognition using micro-Doppler features of radar signals. The experimental campaign involved five different with some similar activities of 10 subjects to be collected using a Doppler radar. A total of several texture features and numerical features are extracted from the spectrogram and time-frequency domain of measurements and applied three machine learning (ML) algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Tree (D-Tree) for the precise classification of all five different motions. The results illustrated that the SVM classifier using the Radial Basis Function (RBF) kernel reaches the best results for all three feature sets, and the classification of mixed features can reach 93.2%, as well as 88.4% and 92.4% for numerical and texture features. The performance of SVM is 1.6% better than KNN and 3.2% better than D-Tree.

Indoor human detection based on micro-Doppler features in the presence of interference from moving clutter sources

In this paper, to address the problem of detecting the presence of human in indoor environments in the presence of moving clutter sources, an indoor human detection method that utilizes random forest to process micro-Doppler signatures and a single pair of TX/RX unit is proposed. In contrast to most of the existing methods that use both distance information and micro-Doppler information assuming no interference from moving clutter sources (window curtains, blinds, table fans, etc.), our proposed method relies only on micro-Doppler information for human detection in indoor environments with curtain and fan interferences. Based on our time–frequency analyses on the measured radar data, seven features, i.e., the mean and standard deviation of the doppler centroid, the mean and standard deviation of the span of envelopes, the silhouette size, the positive peak values, and the peak spread, are extracted from spectrograms. These features are fed into a random forest classifier for categorizing the state of a room into one of the five scenarios considered in this work, namely (1) a person entering the room, (2) a person leaving the room, (3) interference from curtain/blind, (4) interference from fan, and (5) an empty room. The proposed system has been validated using real-world experiments and be able to deliver an accuracy of 97.5% in classifying the scenarios.

Fall Feature Enhancement and Fusion Using the Stockwell Transform With Dual mmWave Radars

Fall detection based on radar technology has recently attracted increasing attention. Human motions are often multidirectional and have complex trajectories. Numerous time–frequency representation methods have been used to extract micro-Doppler fall features. Peak-to-peak Doppler frequency measurements are limited due to the relatively weak energy in the high-Doppler-frequency area. Fall features are not obvious when using a single millimeter-wave (mmWave) radar if human motions are nearly perpendicular to the radar. A fall feature enhancement and fusion method using the Stockwell transform with dual mmWave radars is proposed in this article. Two mmWave radars synchronously collect human motions in different directions, and the micro-Doppler features of these human motions are obtained by using the Stockwell transform. To enhance the features obtained for recognition, the ridgelines extracted from the two radars are fused in the time–frequency domain. In addition, a model trained by a convolutional neural network (CNN) is used for fall recognition; 960 fall motions and 720 nonfall motions are collected from 60 volunteers. The experimental results show that the proposed method can produce a recognition accuracy of up to 94.14%, which is beneficial for the realization of an indoor fall detection system.

Multi-Object Tracking with mmWave Radar: A Review

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

Continuous Human Motion Recognition Based on FMCW Radar and Transformer

Radar‐based human motion recognition has received extensive attention in recent years. Most current recognition methods generate a heat map of features through simple signal processing and then feed into a classification‐based neural network for recognition. Such an approach can only identify a single action. When a set of data contains information about multiple movements, it can also only be recognized as a single movement. Another point that cannot be overlooked is that continuous action recognition methods are able to recognize continuously changing actions but ignore the issue of whether continuous actions are legitimate or not (continuous actions obtained by stitching together multiple current actions do not conform to real time). In this paper, we propose a continuous action recognition method based on micro‐Doppler features and transformer, which translates the micro‐Doppler features of continuous actions into machine translation tasks and uses the idea of natural language processing (NLP) to identify continuous action. In order to judge whether the continuous action is legal or not, we also design the action state transition diagram as a constraint condition to strictly control the forward and backward actions. The experimental results show that the method proposed in this paper achieves good recognition accuracy for the recognition of a single action and can also effectively segment and recognize continuous actions.