Micro-motion Feature Research Articles

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.

A Stationary Clutter Suppression Method for 3-D Micromotion Measurements Based on Wideband Radar Amplitude and Phase Information

The 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 classification and recognition. To achieve high-accuracy three-dimensional micromotion measurements in the clutter environment, a stationary clutter suppression method for three-dimensional micromotion measurements based on wideband radar amplitude and phase information is proposed in this paper. This method is capable of accurately measuring the trajectory of small amplitude micromotion with the high-accuracy phase-derived angle measurement (PDAM) and phase-derived range measurement (PDRM). However, the measurement accuracy of three-dimensional micromotion deteriorates sharply when clutter exists. Thus, the main challenge that we overcome is achieving high-accuracy clutter estimation based on wideband radar measurements. The clutter suppression method is proposed by utilizing wideband radar amplitude and phase of multiple range cells jointly. Finally, simulation and experiment are presented to validate the feasibility and effectiveness of the proposed method under stationary clutter conditions.

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.

Efficient Rotational Angular Velocity Estimation of Rotor Target via Modified Short-Time Fractional Fourier Transform

As a special micro-motion feature of rotor target, rotational angular velocity can provide a discriminant basis for target classification and recognition. In this paper, the authors focus on an efficient rotational angular velocity estimation method of the rotor target is based on the combination of the time–frequency analysis algorithm and Hough transform. In order to avoid the problems of low time–frequency resolution and cross-term interference in short-time Fourier transform and Wigner–Ville distribution algorithm, a modified short-time fractional Fourier transform (M-STFRFT) is proposed to obtain the time-FRFT domain (FRFD)-frequency spectrum with the highest time–FRFD–frequency resolution. In particular, an orthogonal matching pursuit (OMP)-based algorithm is proposed to reduce the computational complexity when estimating the matched transform order in the proposed M-STFRFT algorithm. Firstly, partial transform order candidates are selected randomly from the complete candidates. Then, a partial entropy vector corresponding to partial transform order candidates is calculated from the FRFT results and utilized to reconstruct the complete entropy vector via the OMP algorithm, and the matched transform order can be estimated by searching minimum entropy. Based on the estimated matched transform order, STFRFT is performed to obtain the time–FRFD–frequency spectrum. Moreover, Hough transform is employed to obtain the energy accumulation spectrum, and the micro-Doppler parameter of rotational angular velocity can be estimated by searching the peak value from the energy accumulation spectrum. Both simulated data and measured data collected by frequency modulated continuous wave radar validate the effectiveness of the proposed algorithm.

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.

Feature extraction algorithm of precession target based on image length and Doppler broadening.

In space defense, utilizing the micromotion features to distinguish real targets from interfering targets and decoys is effective. Due to the imaging of the high-speed precession target by microwave radar consisting of isolated scattering centers, there are many difficulties in using inverse synthetic aperture radar (ISAR) images for feature extraction. On the other hand, the inverse synthetic aperture ladar (ISAL) image is relatively continuous because of the short wavelength of laser, and the image sequence contains information about the variation in image length and Doppler width caused by target precession, which can be used for inverse motion parameters. By establishing an observation model of the precession target and performing image processing on the obtained ISAL image at different times, the image length sequence and Doppler width sequence can be obtained. Using the ellipse fitting method to process the obtained sequence, the precession parameters of the target can be obtained. The algorithm does not require prior information such as the radius and speed of the target motion, effectively improving the practicability of the algorithm. Finally, the effectiveness of the algorithm is verified by experimental results, and the error is controlled within 2%.

Data Diven Based Extraction and Recognition of Space Target’s Radar Micromotion Features

The micromotion features of the space target is an important information source of radar target recognition. Multiple micromotions due to certain complex features can further characterize the specific targets and then improve the accuracy of the target recognition. With regard to the extraction and recognition of space target complex micromotion features, the data-driven method was used to conduct effective extraction and training recognition. On the one hand, the EMD method was used to effectively separate the complex micromotion components of the space target, and then the motion parameters of the target are estimated with high precision. On the other hand, we take the motion parameters of the target in various forms of micromotion as input and learn typical object movement characteristics by a machine learning algorithm, thus improve the precision and accuracy of target detection. The simulation results show that EMD method can accurately extract various motion parameters of space complex micromotion targets, and the neural network model can complete the target classification and recognition task with high accuracy.

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.

微动特征提取递归分解快速傅里叶变换算法的研究及应用

微动特征提取递归分解快速傅里叶变换算法的研究及应用

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.

Detection of multi‐people micro‐motions based on range–velocity–time points

The micro-Doppler effect is a useful signature for classifying various human behaviours. However, most micro-Doppler researches assume that only a single moving target exists during the observation. Their works lack in separating micro-motion features from multi-movers. When more than one target is present, their performance will deteriorate heavily. To address this issue, the authors design a new 3D (three-dimensional) model, range-velocity-time points, to separate and describe multi-mover micro-motions measured by the ultra-wideband radar. These 3D points contain the range-velocity-time information simultaneously. By dividing points in the 3D space instead of single Doppler domain, micro-Doppler signatures of each target can be separated effectively. Multi-people motion simulation results verify the effectiveness of the authors' method.

Research on the micromotion characteristics of a chaff cloud

This paper reports the investigation on the micromotion characteristics of chaff dipoles and a chaff cloud. Micromotion models of chaff dipoles and steady-state chaff clouds are discussed. Fourier transform method and time-frequency method are used to study the micromotion features of chaff dipoles, in the energy domain and time-frequency domain, respectively. Based on the characteristic parameters in these two domains, the simulation results show that there are micro-Doppler frequency components in the echo signals from chaff dipoles, and these frequency components vary periodically. Then, the micro-Doppler frequencies of chaff cloud are obtained based on the method of multipoint synthesis. Finally, the experimental results of chaff dipoles dispersion verify the micromotion characteristics of chaff dipoles and chaff cloud obtained by using the empirical mode decomposition method and the short-time Fourier transform method.

Analysis of phase noise influence on micro‐Doppler feature extraction of vibrating target

It is generally considered that increasing the carrier frequency of radar is an important way to improve the precision of micro-motion measurement. However, the increase of the radar frequency may raise the phase noise intensity of the radar transmitting signal and make the extraction more difficult, therefore it is particularly necessary to study the influence of phase noise on the extraction of micro-motion characteristics. In this study, specific studying about the effect of phase noise on the extraction of micro-Doppler (m-D) features is carried out. The effect of phase noise on the extraction performance of the m-D features is evaluated based on a parameter micro-motion signal-to-clutter ratio and an empirical formula is put forward based on the experiments. The results of simulation experiments indicate that increasing the carrier frequency cannot improve the extraction performance of micro-motion features in the case of using both time–frequency analysis method and the new developed sinusoidal frequency modulation Fourier transform (SFMFT) method. Meanwhile, increasing the frequency of target's micro-motion has an improvement effect through time–frequency method due to the increase of Doppler frequency.

Micro‐motion feature extraction of narrow‐band radar target based on ROMP

In this study, a new method based on regularised orthogonal matching pursuit (ROMP) decomposition is proposed to extract the feature of the ballistic target's rotating micro-motion. Based on the establishment of the ballistic target rotation model, the general model of target's narrowband radar echo is obtained. An over-complete dictionary of atoms is established by combining the intrinsic characteristics of the signal model, and the selection of the matched atoms is carried out according to the regularisation principle. The extraction of the target's rotating micro-motion feature and the reconstruction of the original signal are realised. Simulation results show that the method can extract the micro-motion feature efficiently and accurately.

Micro‐motion feature extraction of space targets based on sinusoidal frequency modulation Fourier transform

The space target can be discriminated from other objects by the micro-motion feature difference. The micro-motion features of a space target with precession include the spinning period, the precession angle and the precession period etc. The main work of this study is to extract the target micro-motion period from the echo by sinusoidal frequency modulation Fourier transform (SFMFT). The proposed method takes the advantages of the phase terms operation of the SFMFT. In the study, the micro-Doppler effect induced by precession is analysed and then present the algorithm step by step. Meanwhile, the simulation is given to demonstrate the validity of the proposed method.

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.

Precession parameter estimation method of the cone‐shaped target based on inverse synthetic aperture radar image sequence analysis

The micro-motion features induced by space targets provide effective information for target classification and recognition. This study introduces the equivalent rotational velocity (ERV) estimation for precession parameter estimation of the cone-shaped target with inverse synthetic aperture radar image sequence. On the basis of the rotation matching relationship of precession cone-shaped target, the estimated ERV is used to build an eigenfunction, and the parameter searching set is obtained. Through theoretical analysis, the coupled parameters of the eigenfunction will concentrate on a curve on the searching plane. Thus, the parameter searching set is reduced to the neighbouring area of this curve, which reduces the computational burden greatly. Finally, the parameters are decoupled by the reconstructed radar observation angle, and the precession parameters such as precession period, precession angle, average observation angle, radial length and bottom radius of the target are precisely estimated. The proposed algorithm requires no prior information and it has high computational efficiency. Simulation results with electromagnetic data illustrate the validity of the proposed method.

A Novel High-Precision Range Estimation Method Based on Phase of Wideband Radar Echo

High-precision parameter estimation is crucial for micromotion feature acquisitions. A method of high-precision range estimation based on phase of wideband radar echo is proposed in this paper. After the proposed processing, unwrapped signal phases without any ambiguity can be obtained to calculate the relative range of the target. No constraint condition for phase ambiguity resolution is imposed on the proposed method, which means that it could still work well in low signal-to-noise ratio situations. Cramer–Rao lower bound for the root-mean-squared error of the range estimation is derived in analytical expression. Results of several simulations and experiment with FEKO generated data are presented to show the effectiveness and antinoise performance of the proposed method.