Micro-motion Feature Research Articles

Realizing Small UAV Targets Recognition via Multi-Dimensional Feature Fusion of High-Resolution Radar

For modern radar systems, small unmanned aerial vehicles (UAVs) belong to a typical types of targets with ‘low, slow, and small’ characteristics. In complex combat environments, the functional requirements of radar systems are not only limited to achieving stable detection and tracking performance but also to effectively complete the recognition of small UAV targets. In this paper, a multi-dimensional feature fusion framework for small UAV target recognition utilizing a small-sized and low-cost high-resolution radar is proposed, which can fully extract and combine the geometric structure features and the micro-motion features of small UAV targets. For the performance analysis, the echo data of different small UAV targets was measured and collected with a millimeter-wave radar, and the dataset consists of high-resolution range profiles (HRRP) and micro-Doppler time–frequency spectrograms was constructed for training and testing. The effectiveness of the proposed method was demonstrated by a series of comparison experiments, and the overall accuracy of the proposed method can reach 98.5%, which demonstrates that the proposed multi-dimensional feature fusion method can achieve better recognition performance than that of classical algorithms and higher robustness than that of single features for small UAV targets.

Research on multi-dimensional micro-motion feature extraction of moving targets

The micro-Doppler effect is a physical phenomenon generated by the micro-motion of objects and their components, which have a significant influence on improving radar detection and resolution capability and also enhancing the radar imaging and target recognition performance. The extraction of micro-Doppler frequency, as a commonly used time-frequency analysis tool, is of great significance in extracting and reconstructing the signal with micro-motion targets. The micro-motion characteristics for moving targets can be verified by using simulation through combining the theory of micro-Doppler effect with the frequency domain model of electromagnetic waves. The simulation research on the micro-motion characteristics of a three-dimensional target is conducted by using the finite element method. The influences of environmental conditions such as relative humidity, visibility, and the presence or absence of turbulence on echo intensity and time-frequency relationship are investigated theoretically. The simulation results indicate that parameters such as relative humidity and visibility, which affect the atmospheric attenuation coefficient, can reduce echo intensity and the period of time-frequency curve. By triggering off beam drift in the transmission path, turbulence can lead to “frequency shift deformation” of the time-frequency curve, degrading the extraction of target motion attitude. A motion attitude classification method is proposed in order to study the micro-Doppler effect better. According to whether the frequency shift changes with time, the motion attitude can be divided into frequency shift time-invariant motion and time-variant motion. Frequency shift time-variant motion includes translation, rolling and vibration. Vibration and rolling are motions that periodically change with time, requiring the comparison of instantaneous frequency shifts at any three times within a cycle. Translation is a time-variant motion with irregular frequency shifts over time, which involves studying instantaneous frequency shifts at any three times. Transient frequency shifts should be analyzed and compared at different times for these motions. The frequency shift time-invariant motion is mainly rotation obtained experimental results indicate that the amplitude, plus-minus, and spectral width of frequency shift at different positions are aimed at inverting the target shape, attitude, direction and velocity. Demodulating one-dimensional data obtained from the FFTshift function can obtain the time-frequency-intensity relationship. This multi-parameter analysis method is a multi-dimensional processing method widely used in the fields of radar, sonar, and communication. The above research is conductive to the measurement of target macroscopic shape properties and the extraction of microscopic motion information, which lays the foundation for radar detection and recognition.

A method for extracting micro-motion features of rotor targets based on GS-IRadon algorithm

Aiming the extract micro-motion features of rotor targets when the flashes are in the echo, this paper proposes a method based on the GS-IRadon algorithm. At first, we analyze the time-frequency characteristics of rotor targets through a scattering point signal model. Then, we analyze and study the usage conditions of the algorithm and use the Moving Target Indication (MTI) algorithm to remove the direct-current (DC) frequency component in signal's time-frequency results. Finally, we utilize the Inverse Radon transform algorithm based on the golden section method (GS-IRadon) to extract the target micro-motion features. The estimation of speed, length, and number of blades in the presence of flashes phenomenon in rotor targets was achieved. Simulation results showed that the algorithm can significantly reduce computational complexity while maintaining high estimation accuracy and has a more stable performance than regular algorithms.

Scatterer-Level Time-Frequency-Frequency Rate Representation for Micro-Motion Identification

Radar micro-motion signatures help to judge the target’s motion state and threat level, which plays a vital role in space situational awareness. Most of the existing micro-motion feature extraction methods derived from time-frequency (TF) representation cannot simultaneously satisfy the requirements of high resolution and multiple component representation, which has limitations on processing intersected multi-component micro-motion signals. Meanwhile, as the micro-motion features extracted from the TF spectrograms only focus on the global characteristics of the targets and ignore the physical properties of micro-motion components, it leads to poor performance in micro-motion discrimination. To address these challenges, we empirically observed a decrease in the probability of intersection between the components within the time-frequency-frequency rate (TFFR) space, where components appeared as separated and non-intersecting spatial trajectories. This observation facilitates the extraction and association of multiple components. Given the differences in modulation laws among various micro-motions in the TFFR space, we introduced a novel micro-motion identification method based on scatterer-level TFFR representation. Our experimental evaluations of different targets and micro-motion types demonstrate the efficacy and robustness of this proposed method. This method not only underscores the separability of signal components but also expands the scope of micro-motion discrimination within the TFFR domain.

Micromotion Feature Extraction with VEMW Radar Based on Rotational Doppler Effect

Micro-Doppler (m-D) analysis is the most effective mechanism for detecting rotating targets or components; however, it fails when the target rotation plane is perpendicular to the radar line of sight (LOS). The vortex electromagnetic wave (VEMW) provides a unconventional structure of wavefront phase modulation on the cross-plane of the radar LOS, on which the radial m-D vanishes while the rotational Doppler (RD) appears. In the absence of the position of rotation center, this paper focuses on the micromotion parameters estimation based on RD effect for rotating target, and then proposes an estimation procedure, referred to as the two-step method. The micromotion parameters of the rotating target include the rotation attitude, the rotation radius and the position of the rotation center while the latter is coupled to the former two. Firstly, the micromotion parameters are roughly estimated based on the RD curve parameters obtained from the time-frequency (TF) spectrum of the received signal. Secondly, the maximum likelihood estimation (MLE) is used to accurately estimate the micromotion parameters. In addition, the Cramér–Rao bound (CRB) of parameter estimation is derived. The simulation studies the influencing factors of estimation performance and verifies that the proposed estimation method can provide excellent estimation accuracy of the micromotion parameters.

Detection Method of Radar Space Target Abnormal Motion via Local Density Peaks and Micro-Motion Feature

Micro-motion feature vectors of space targets are usually unevenly and multi-cluster distributed, which limits the performance of traditional radar anomaly detection methods. To solve this problem, a novel detection method of radar space target abnormal motion method via local density peaks and micro-motion feature is proposed in this paper. Firstly, two discriminative micro-motion features are extracted from the radar echoes to construct a 2-D feature space. Then the abnormal motion detector is derived by classifying the feature vectors into different clusters according to the local density peaks and minimum spanning tree clustering, and solving for the decision thresholds of each cluster with the local density peaks, neighbors and some preset false alarm rates. Electromagnetic simulation experiment results demonstrate that the detection rate of the proposed method is 2.49%, 5.26%, 9.63%, 15.37%, 27.99% and 49.45% higher than six state-of-art methods respectively when the false alarm rate is 5%.

High-resolution imaging and micro-motion feature extraction of space multiple targets

In radar applications, different micro-motion forms can be used as the basis of target recognition. However, the radar echo signal of multiple spatial targets is overlapping in the time-frequency and time-range domains, which increases the difficulty of micro-motion feature extraction. In this paper, a high-resolution imaging and micro-motion feature extraction framework based on a multiple joint-domain radar tool is proposed to address this mixed signal. First, an accurate and suitable micro-motion model of cone-shaped space multiple targets is built. Then, the two-dimensional adaptive regularized smoothed L0 norm algorithm (2D AReSL0) based on sparse reconstruction is formulated to directly reconstruct the inverse synthetic aperture radar (ISAR) image. Additionally, the range-frequency-time radar data cube can be extracted from the ISAR movie by the CLEAN algorithm. To solve the scattering point association problem in the radar data cube, a 3D segmentation Viterbi algorithm is designed to extract the micro-motion features. Finally, simulation and experiment results demonstrate the effectiveness of the proposed framework.

Temporal aggregation network using micromotion features for early lameness recognition in dairy cows

Lameness severely affects dairy cow welfare and potential milk production while reducing dairy farm economics. Most existing computer vision-based lameness recognition methods extract a single class of lameness features, and it is difficult to accurately recognize early lameness in dairy cows with only slight gait abnormalities. In this paper, a temporal aggregation network using micromotion features is proposed, which can capture micromotion features and spatiotemporal features to recognize early lameness in dairy cows. First, a Gaussian filter is used to remove noise in videos of a dairy cow walking. Second, frame-difference images containing motion features are constructed by the frame-difference method. Third, micromotion features in the frame-difference images are extracted using the designed focal convolutional layer. Fourth, the acquired micromotion feature sequence is input into temporal attention mechanisms to extract multigranularity temporal features. Finally, a fully connected layer is used to classify the dairy cow lameness. To evaluate the performance of the proposed model, 621 videos were randomly selected from 888 videos as training videos, 87 videos were randomly selected as validation videos, and the remaining 180 videos were selected as test videos. The lameness recognition accuracy of the proposed model was 98.89%, which was better than state-of-the-art lameness recognition methods. The experimental results showed that the proposed model can extract the micromotion features and multigranularity temporal features in the frame-difference images, thereby improving the early lameness recognition accuracy in dairy cows.

Ballistic Target Recognition Based on 4-D Point Cloud Using Randomized Stepped Frequency Radar

Ballistic target recognition (BTR) is critical to the ballistic missile defense system. The challenge of this task is to distinguish warheads from numerous unknown confusing targets within a short observing time. The micromotion feature is proved to be effective for this task. However, traditional methods need a long observing time to acquire enough information for the recognition because of using low-dimensional features. In addition, these model-driven methods cannot handle irregular ballistic targets, such as debris. In this article, we propose a BTR scheme, which characterizes the micromotion features with a higher dimensional representation, i.e., the time–range–velocity–power 4-D point cloud, using the randomized stepped frequency radar. The higher dimensional information contained in the 4-D point cloud can reduce the required observing time. Besides, this scheme combines the model-driven method with a data-driven deep neural network to meet the challenge of model mismatch caused by irregular targets. As a result, the proposed BTR scheme is time efficient and robust, which has been proved on an electromagnetic simulation dataset.

Hand Gesture Recognition Using Radial and Transversal Dual Micromotion Features

Most of the work in hand gesture recognition (HGR) focuses on developing diverse classification algorithms based on micro-Doppler (mD) spectrogram, that is 1-D motion along the radial direction. In this work, we exert effort on the radar system and preprocessing methods to extract 2-D motions for HGR. Specifically, we utilize an interferometric radar with two widely spaced receivers to obtain both radial and transversal micromotion features of hand gestures. In the preprocessing stage, as pre-interferometry is nonlinear multiplication in time domain, both the increased noise level and unwanted cross-terms may reduce its usefulness for HGR. To solve these problems, we propose a post-interferometric preprocessing method in frequency domain, which is capable of reducing noise level of the obtained spectrogram and suppressing the nuisance cross-terms. We measure four pairs of symmetric hand gestures from three persons and compare the HGR accuracy using different preprocessing methods. Experimental results show that the mD processing combined with post-interferometry give the highest HGR accuracy of over 99%.

Micro-Motion Parameter Extraction of Multi-Scattering-Point Target Based on Vortex Electromagnetic Wave Radar

In addition to traditional linear Doppler shift, the angular Doppler shift in vortex electromagnetic wave (VEMW) radar systems carrying orbital angular momentum (OAM) can provide more accurate target identification micro-motion parameters, especially the detailed features perpendicular to the radar line-of-sight (LOS) direction. In this paper, a micro-motion feature extraction method for a spinning target with multiple scattering points based on VEMW radar is proposed. First, a multi-scattering-point spinning target detection model using vortex radar is established, and the mathematical mechanism of echo signal flash shift in time-frequency (TF) domain is deduced. Then, linear Doppler shift is eliminated by interference processing with opposite dual-mode VEMW. Subsequently, the shift in TF flicker is focused on the reference zero frequency by the iterative phase compensation method, and the number of scattering points is estimated according to the focusing effect. After this, through the constructed compensation phase, the angular Doppler shift is separated, then the angular velocity, rotation radiusand initial phase of the target are estimated. Theoretical and simulation results verify the effectiveness of the proposed method, and more accurate rotation parameters can be obtained in the case of multiple scattering points using the VEMW radar system.

Recognition of Ballistic Targets by Fusing Micro-Motion Features with Networks

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.

An Adaptive and Accurate Method for Rotational Angular Velocity Estimation of Rotor Targets via Fourier Coefficient Interpolation

As a special micro-motion feature of rotor targets, rotational angular velocity can provide a discriminant basis for target classification and recognition. In this paper, an adaptive and accurate method is proposed for estimating the rotational angular velocity of rotor targets via a Fourier coefficient interpolation algorithm that is based on modified frequency index residue initialization. The negative frequency complex exponential signal component is removed at each iteration to eliminate the estimation bias caused by spectrum superposition and improve the estimation accuracy. The frequency index residue initialization is modified, based on a normalized Fourier spectrum, to improve the estimable range of the rotational angular velocity and reduce the computational complexity of the algorithm. The simulation results show that the estimation performance of the proposed method achieves the Cramér–Rao lower bound and outperforms state-of-the-art methods in terms of the estimable range and estimation accuracy of the rotational angular velocity.

Micro-Doppler Feature Extraction of Rotating Structures of Aircraft Targets with Terahertz Radar

The micro-Doppler features formed by the micro-motion of rotating blades of rotors and turbines are of great significance for aircraft target detection and recognition. Mastering the micro-motion features is the premise of radar target identification. The blades’ length and rotation rate are vital parameters for classifying aircraft targets. One can instantly judge the type and state of the targets by extracting micro-Doppler features. To extract the micro-Doppler features of rotating blades of the turbine target, we utilized microwave-band and terahertz-band radar to simulate the target and extract the Doppler frequency-shift information. For a turbine model with an obvious blade tip structure, we propose an algorithm based on wavelet coefficient enhancement and inverse Radon transform, integrating the time–frequency analysis with image processing. Under low SNR, this method allows for a high-accuracy parameter estimate. For a two-bladed rotor model without an obvious blade tip structure, we conducted an actual measurement experiment on the model utilizing a 120 GHz radar, and we propose a parameter estimation algorithm based on the fitting of the time–frequency distribution. By fitting the data of the time–frequency diagram, the micro-motion characteristic parameters of the rotor target were obtained. The simulation and experimental results demonstrate the benefits of terahertz radar in target detection, and indicate that the proposed algorithms have the characteristics of high extraction precision and insensitivity to noise.

Clutter Suppression and Rotor Blade Feature Extraction of a Helicopter Based on Time–Frequency Flash Shifts in a Passive Bistatic Radar

This paper presents a passive bistatic radar (PBR) configuration using a global navigation satellite system as an illuminator of opportunity for the rotor blade feature extraction of a helicopter. Aiming at the strong fixed clutter in the surveillance channel of the PBR, a novel iteration clutter elimination method-based singular-value decomposition approach is proposed. Instead of the range elimination method used in the classic extended cancellation algorithm, the proposed clutter elimination method distinguishes the clutter using the largest singular value and by remove this value. At the same time, the fuselage echo of the hovering helicopter can also be suppressed along with the ground clutter, then the rotor echo of this can be obtained. In the micro-motion feature extraction, the mathematic principle of the flash generation process in the time–frequency distribution (TFD) is derived first. Next, the phase compensation method is applied to achieve the time–frequency flash shift in the TFD. After this, the center frequencies of the standard flashes in the TFD are compared with the standard frequency dictionary. The mean l1 norm is utilized to estimate the feature parameters of the helicopter rotor. In the experiments, the scattering point model and the physical optics facet model demonstrate that the proposed method can obtain more accurate parameter estimation results than some classic algorithms.

Translational Compensation Algorithm for Ballistic Targets in Midcourse Based on Template Matching

The high-speed movement of a ballistic target will cause folding and translation of the micro-Doppler, which will affect the extraction of micro-motion features. To address the adverse effects of high-speed movement of ballistic targets in midcourse on the extraction of micro-motion features, a novel translational compensation algorithm based on template matching is proposed. Firstly, a 512 × 512 time-frequency map is obtained by binarization and down-sampling. The matching template then convolves the time-frequency map to obtain contour-like points. Then, the upper and lower contour points are preliminarily determined by the extreme value, and all actual contour points are screened out through structural similarity. Lastly, the upper and lower trend lines are determined and translation parameters for compensation by polynomial fitting are estimated. Simulation results show that the proposed algorithm has lower requirements for time-frequency resolution, higher precision and lower time complexity as a whole. Furthermore, it is also applicable to spectral aliasing.

Micro-Doppler Parameters Extraction of Precession Cone-Shaped Targets Based on Rotating Antenna

Micro-Doppler is regarded as a unique signature of a target with micro-motions. The sophisticated recognition of the cone-shaped targets can be realized through the micro-Doppler effect. However, it is difficult to extract the micro-motion features perpendicular to the radar line of sight (LOS) effectively. In this paper, a micro-Doppler parameters extraction method of the cone-shaped targets is put forward based on the rotating antenna. First, a new radar configuration is proposed, in which an antenna rotates uniformly on a fixed circle, thus producing Doppler frequency shift. Second, the expression of the micro-Doppler frequency shift induced by the precession cone-shaped target is derived. Then, the micro-Doppler curves of point scatterers at the cone top and bottom are separated by the smoothness of the curves, and the empirical mode decomposition (EMD) method is utilized for the detection and estimation of the coning frequency. Finally, the micro-motion components perpendicular to the radar LOS are inverted by the peak of micro-Doppler frequency curve. Simulation results prove the effectiveness and robustness of the proposed method.

Micro-Motion Classification of Flying Bird and Rotor Drones via Data Augmentation and Modified Multi-Scale CNN

Aiming at the difficult problem of the classification between flying bird and rotary-wing drone by radar, a micro-motion feature classification method is proposed in this paper. Using K-band frequency modulated continuous wave (FMCW) radar, data acquisition of five types of rotor drones (SJRC S70 W, DJI Mavic Air 2, DJI Inspire 2, hexacopter, and single-propeller fixed-wing drone) and flying birds is carried out under indoor and outdoor scenes. Then, the feature extraction and parameterization of the corresponding micro-Doppler (m-D) signal are performed using time-frequency (T-F) analysis. In order to increase the number of effective datasets and enhance m-D features, the data augmentation method is designed by setting the amplitude scope displayed in T-F graph and adopting feature fusion of the range-time (modulation periods) graph and T-F graph. A multi-scale convolutional neural network (CNN) is employed and modified, which can extract both the global and local information of the target’s m-D features and reduce the parameter calculation burden. Validation with the measured dataset of different targets using FMCW radar shows that the average correct classification accuracy of drones and flying birds for short and long range experiments of the proposed algorithm is 9.4% and 4.6% higher than the Alexnet- and VGG16-based CNN methods, respectively.

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.

A Novel Approach of Extracting Ship Scatterer's 3-DOF Micromotion Features Under Long Accumulation Time

A Novel Approach of Extracting Ship Scatterer's 3-DOF Micromotion Features Under Long Accumulation Time