Micro-motion Feature Extraction Research Articles

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.

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.

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.

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.

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.

A Phase-Derived Velocity Measurement Method Based on the Generalized Radon–Fourier Transform With a Low SNR

The phase-derived velocity measurement (PDVM) technique can achieve a high measurement accuracy at the phase level and thus has great application prospects in the field of micromotion feature extraction and target recognition. To achieve a PDVM with a low signal-to-noise ratio (SNR), a PDVM method based on the generalized Radon–Fourier transform (GRFT) is proposed in this article. The main challenges that we overcome are phase extraction and phase ambiguity resolving under the condition of a low SNR. By utilizing the GRFT to estimate the target motion parameters, the echo peak position can be reconstructed, and then the peak phase value can be extracted. In the meantime, the phase ambiguity integer can be resolved based on the rough velocity estimation results obtained by the GRFT, and the phase ambiguity resolving can be realized at a low SNR. In addition, to suppress the influence of noise on the extracted phase, a filter design method based on the target motion characteristics is proposed to further improve the accuracy of the PDVM. In the simulation, the performance of the proposed method under different motion models and different SNR conditions is analyzed, and the effectiveness of the proposed method under low-SNR conditions is verified. Compared with directly using the GRFT, the proposed method has the advantages of strong applicability to different motion models and low computational load.

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.

Micro-Doppler Separation Based on U-Net and Plot-Curve Association for Ballistic Target

The high-precision separation of micro-Doppler curves is the key to micro-motion feature extraction and parameter estimation for ballistic target in midcourse. The micro-Doppler curves of each scatter overlap seriously in the time-frequency domain and are also affected by nonideal scattering phenomena such as strong noise and occlusion effects, which poses a significant challenge to the traditional curve separation methods. Aiming at this problem, a micro-Doppler curve separation algorithm under nonideal scattering conditions is proposed in this paper. First, the micro-Doppler curve and noise are separated through the U-Net model in the time-frequency domain. Then on the basis of eliminating the effect of redundant and pseudo plots by plot condensation and plot processing, the micro-Doppler plots are associated and regrouped based on interpolation and curve smoothness function. Finally, the effectiveness and robustness of the proposed algorithm have been illustrated by extensive simulation experiments.

Analysis and Research on Radar Gesture Micro-motion Feature Extraction Method

Radar gesture recognition technology is widely used in daily life, and the micro motion feature is the key information to distinguish the feature quantity of different gestures. The extraction of micro motion feature is the key step of radar gesture recognition. Joint time-frequency analysis is a common method for radar signal micro motion feature extraction. Firstly, this paper makes a theoretical analysis of the common joint time-frequency analysis methods; Then radar gesture model is completed, and the performance of different JTFA is compared by extracting the time-frequency features of the motion gesture echo; Finally, the measured data are collected under the condition of microwave anechoic chamber. After analysis, the theoretical simulation results are consistent with the measured data, which verifies the reliability of the simulation.

A Noise Robust Micro-Range Estimation Method for Precession Cone-Shaped Targets

The estimation of micro-Range (m-R) is important for micro-motion feature extraction and imaging, which provides significant supports for the classification of a precession cone-shaped target. Under low signal-to-noise ratio (SNR) circumstances, the modified Kalman filter (MKF) will obtain broken segments rather than complete m-R tracks due to missing trajectories, and the performance of the MKF is restricted by unknown noise covariance. To solve these problems, a noise-robust m-R estimation method, which combines the adaptive Kalman filter (AKF) and the random sample consensus (RANSAC) algorithm, is proposed in this paper. The AKF, where the noise covariance is not required for the estimation of the state vector, is applied to associate m-R trajectories for higher estimation accuracy and lower wrong association probability. Due to missing trajectories, several associated segments which are parts of the m-R tracks can be obtained by the AKF. Then, the RANSAC algorithm is utilized to associate the segments and the complete m-R tracks can be obtained. Compared with the MKF, the proposed method can obtain complete m-R tracks instead of several segments, and avoids the influence of unknown noise covariance under low SNR circumstances. Experimental results based on electromagnetic simulation data demonstrate that the proposed method is more precise and robust compared with traditional methods.

Obtaining TFR From Incomplete and Phase-Corrupted m-D Signal in Real Time

In micromotion feature extraction, the incomplete and phase-corrupted radar echo may cause bad time–frequency representation (TFR) and prevent micromotion feature extraction. To solve the problem, we establish a sparse regularization model to reconstruct well-focused TF distributions. The regularization model is solved by the iterative soft-thresholding algorithm (ISTA). In each iteration, the hard thresholding function and least-square-error criterion are developed to estimate the phase errors. For micro-Doppler signal real-time processing to save radar time resources, the received signal can be directly sparse recovered in real time rather than waiting for the complete signal. Finally, the effectiveness of the proposed method is validated by the simulation results.

Target Signal Separation of Missile Group Based on Micro-motion Frequency Correlation

Object micro-motion feature extraction method for isolated targets is relatively mature, but the resolution and micro-motion feature extraction method for group targets still needs further research. Ballistic missiles in the middle of the throw-off process is taken to be the example, when the false target is released. A method of target separation by micro-Doppler frequency is proposed, and the target is modeled. A parameterized expression is given. The narrow band echo is processed by STFT and the time-frequency plane is obtained. After that, the BM3D algorithm is used to filter and reduce noise to smooth the curve. The improved Viterbi algorithm is used to extract the micro-Doppler curve of the group target. Finally, the Fourier transform is performed. According to the frequency correlation, the group target is separated, and the simulation verified this method.

Three-Dimensional Micro-Motion Feature Extraction of the Vibrating Target Based on Multi-Channel Radar in the Terahertz Band.

Vibration induces a micro-Doppler effect, which contains crucial motion information and features of the micro-motion target. However, the traditional radar can only acquire micro-motion information in the radial direction. The multi-channel radar is capable of acquiring actual micro-motion information in a three-dimensional space, including amplitude, frequency, and three-dimensional direction of vibration. Moreover, the high-accuracy parameter estimation can be achieved in the terahertz band. In this paper, a method for three-dimensional micro-motion feature exaction and parameter estimation is proposed, including a phase-derived-range algorithm, interference, and auto-correlation. Meanwhile, to prove feasibility of the method, the results of simulations and experiment based on a 0.22 THz multi-channel radar system are presented after theoretical analyses.

Micro-Motion Feature Extraction of a Rotating Target Based on Interrupted Transmitting and Receiving Pulse Signal in an Anechoic Chamber

The pulse signal is widely used in micro-motion feature extraction of rapidly rotating targets as its pulse repetition frequency (PRF) can be high. However, when the pulse signal is implemented in a range-limited anechoic chamber for micro-motion feature extraction, the transmitted and reflected pulse signals may be coupled at the receiver. To solve this problem, the interrupted transmitting and receiving (ITR) method is applied to transmit the pulse signal with hundreds of sub-pulses. The target echo can be received when the sub-pulse is not transmitted. Hence, it avoids the coupling effect of transmitted signals and echoes. Then, the whole process of micro-motion feature extraction for rotating target is proposed based on the ITR method. At last, the simulations and experiments verify that the rotating target micro-Doppler can be extracted by the ITR pulse signal.

Feature extraction of precession target based on Doppler profile sequences by maximum likelihood estimation

The micro-motion feature extraction of precession target is an effective way for discriminating warhead from decoy in missile ballistic defence. Generally, traditional methods based on high-resolution range profile (HRRP) sequences put forward high requirements on modulation bandwidth for the system, increasing the complexity of the system significantly. Utilising single-frequency ladar without modulation, the authors proposed a precession parameter extraction algorithm of precession target based on high-resolution Doppler profile (HRDP) sequences. Firstly, they model the Doppler bandwidth of the cone-shaped precession target with the observation angle. Under the assuming that the measurement error obeys zero-mean Gaussian distribution, the probability density function of Doppler bandwidth is established. Then, the precession parameters are estimated with measured Doppler profile sequences through maximum likelihood estimation method. Compared with the methods based on HRRPs, the proposed method based on the Doppler profile has lower requirements on the system and easier to implement. Laboratorial data has been used to validate the feasibility of the proposed method.

Micromotion Feature Extraction Based on Phase-Derived Range and Velocity Measurement

Micromotion features of a target contain unique structural information and motion information about the target, which can be used as an important basis for target recognition. One of the technical bottlenecks for micromotion feature extraction is achieving a fine characterization of the target motion state. With the high-precision range and velocity measurement, this paper uses the phase-derived method to reconstruct the motion state of the target and realize accurate translational compensation for the target. At the same time, the high range resolution of wideband radar is used to effectively extract target scattering points with micromotion from the perspective of the one-dimensional high range resolution profile and the two-dimensional inverse synthetic aperture radar (ISAR) image. On this basis, reliable micromotion feature extraction can be achieved by using the slow variability of the target motion and eliminating the translational or rotational information of the target. The performance of the high-precision range and velocity measurement and the feasibility of the micromotion feature extraction based on phase-derived range and velocity measurement are verified by simulation results. Then, the effectiveness of the proposed algorithm is further verified by carrying out a steel ball ejection experiment and a civil aviation aircraft experiment.

Three-dimensional interferometric imaging and micromotion feature extraction of spinning space debris in low-resolution radar

Inspired by the idea of multiantenna interferometric processing in interferometric inverse synthetic aperture radar, based on monostatic low-resolution narrowband radar with three antennas, a three-dimensional (3-D) interferometric imaging and micromotion feature extraction method for spinning space debris is proposed. First, the micromotion characteristic of the target in low-resolution radar is explained. Next, by taking advantage of the target’s micro-Doppler characteristic on time-frequency (TF) domain, the time-varying two-dimensional imaging of scatterers in both the normal mode and the squint mode is achieved through interferometric processing. Finally, the height information of scatterers is solved by an ellipse fitting-based method; thus 3-D imaging for the spinning space debris is realized. Meanwhile, some 3-D micromotion features are exactly extracted during imaging, and the relevant issues, including window width determination of TF analysis, scatterer separation, and noise suppression are discussed in detail. Simulation results in diverse occasions validate the effectiveness and the robustness of the method.

Three-dimensional interferometric imaging and precession feature extraction of space targets in wideband radar

Three-dimensional (3-D) imaging and the 3-D micromotion feature extraction technique of space targets provide significant information for target classification and recognition. The idea of multiantenna interferometric processing from the interferometric inverse synthetic aperture radar (InISAR) is introduced into the 3-D imaging of micromotional space targets. After the modeling analysis of the micromotion characteristic induced by the target on the range–slow-time plane, a 3-D interferometric imaging method for space precession targets in wideband radar is proposed. The real 3-D images of the target in both the normal mode and the squint mode are obtained by the proposed method. Meanwhile, some 3-D precession features and structure parameters of the target are extracted accurately. Finally, simulation results validate the effectiveness and robustness of the proposed method. Compared with the existed approaches, this study can achieve real 3-D imaging and extract precession features and structure parameters for space precession target only by a monostatic radar with multi-antenna.

Adaptive Resource Allocation Scheme for Micromotion Feature Extraction Based on Track-Before-Detect

The micromotion feature extraction method based on track-before-detect (TBD) can save the radar resource and improve the real-time performance of micromotion feature extraction by implementing target detecting, tracking, and micromotion feature extraction simultaneously. Usually, multitargets will exist in different areas, and the limited radar resources should be allocated for different areas to achieve the maximal performance of radar. For single-beam phased array radar, an adaptive resource allocation optimization model is established according to the processing steps of the micromotion feature extraction method based on TBD, and an adaptive resource allocation strategy is proposed. With the method, the radar efficiency can be significantly improved. The effectiveness of the proposed method is demonstrated by simulations.