Micro-motion Parameters Research Articles

Recognition of precession angles of non-cooperative targets based on deep learning with privileged information

With an increasing number of countries engaging in space activities worldwide, space non-cooperative target tracking and identification technology has become a prerequisite for safely conducting space operations. In order to the identify distant non-cooperative targets performing complex motions, this paper proposes a method to recognize difficult parameters by using easily available signal labels as privileged information, which is named Pi-FcResNet. The privileged information is connected to the output end of the network through a fully connected network and coupled with the linear layer of the main network. Through testing, our network achieved a recognition accuracy of 94.45 % for precession angles under high signal-to-noise ratio conditions. After incorporating the Convolutional Block Attention Module (CBAM), our method demonstrates fast fitting speed and robust performance. Testing on experimental data shows that, compared to traditional methods, our approach offers better stability and reproducibility in recognizing micro-motion parameters. This approach of using known information as additional information for deep learning networks holds great potential in the field of feature extraction for space non-cooperative targets undergoing complex motions.

Decoupling and Parameter Extraction Methods for Conical Micro-Motion Object Based on FMCW Lidar.

Micro-Doppler time-frequency analysis has been regarded as an important parameter extraction method for conical micro-motion objects. However, the micro-Doppler effect caused by micro-motion can modulate the frequency of lidar echo, leading to coupling between structure and micro-motion parameters. Therefore, it is difficult to extract parameters for micro-motion cones. We propose a new method for parameter extraction by combining the range profile of a micro-motion cone and the micro-Doppler time-frequency spectrum. This method can effectively decouple and accurately extract the structure and the micro-motion parameters of cones. Compared with traditional time-frequency analysis methods, the accuracy of parameter extraction is higher, and the information is richer. Firstly, the range profile of the micro-motion cone was obtained by using an FMCW (Frequency Modulated Continuous Wave) lidar based on simulation. Secondly, quantitative analysis was conducted on the edge features of the range profile and the micro-Doppler time-frequency spectrum. Finally, the parameters of the micro-motion cone were extracted based on the proposed decoupling parameter extraction method. The results show that our method can effectively extract the cone height, the base radius, the precession angle, the spin frequency, and the gravity center height within the range of a lidar LOS (line of sight) angle from 20° to 65°. The average absolute percentage error can reach below 10%. The method proposed in this paper not only enriches the detection information regarding micro-motion cones, but also improves the accuracy of parameter extraction and establishes a foundation for classification and recognition. It provides a new technical approach for laser micro-Doppler detection in accurate recognition.

Rotational Doppler Extraction and Micromotion Parameters Estimation of Rotating Target with OAM Beams

Rotational Doppler Extraction and Micromotion Parameters Estimation of Rotating Target with OAM Beams

Spectral De-Aliasing Method of Micro-Motion Signals Based on a Complex-Valued U-Net Network

Spectrum aliasing occurs in signal echoes when the sampling frequency does not comply with the Nyquist Sampling Theorem. In this scenario, the extraction of micro-motion parameters becomes challenging. This paper proposes a spectral de-aliasing method for micro-motion signals based on a complex-valued U-Net network. Zero interpolation is employed to insert zeros into the echo, effectively increasing the sampling frequency. After zero interpolation, the micro-motion signal contains both real micro-motion signal frequency components and new frequency components. Short-Time Fourier Transform (STFT) is then applied to transform the zero-interpolated echo from the time domain to the time–frequency domain. Furthermore, a complex-valued U-Net training model is utilized to eliminate redundant frequency components generated by zero interpolation, thereby achieving the frequency reconstruction of micro-motion signal echoes. Finally, the training models are employed to process the measured data. The theoretical analysis, simulations, and experimental results demonstrate that this method is robust and feasible, and is capable of addressing the problem of micro-motion signal echo spectrum aliasing in narrowband radar.

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.

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.

Real Micro-Doppler Parameters Extraction of Spinning Targets Based on Rotating Interference Antenna

Micro-Doppler is a unique characteristic of targets with micro-motions, which can provide significant information for target classification and recognition. However, the monostatic radar has the shortcoming of only obtaining the radial micro-motion characteristics. Although the vortex-electromagnetic-wave-based radar has the potential to obtain real micro-motion parameters, it has a high dependence on the mode number and purity of the orbital angular momentum, which greatly restricts its application in the micro-motion parameter extraction. To overcome the above problems, a new radar configuration based on the rotating interference antenna is proposed in this paper. Through the interference processing of the micro-Doppler curves of the rotating and fixed antenna, the curves containing the real micro-motion information of the target can be obtained. Then the real micro-motion characteristics of the spinning target can be reconstructed by the orthogonal matching pursuit algorithm. The effectiveness and robustness of the proposed method are validated by simulations.

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

It is of great significance to detect drones in airspace due to the substantial increase and regrettable misuse in the consumer market. In this paper, we establish a micro-motion theoretical model of a drone and analyze the micro-Doppler signature of rotor targets and the flicker mechanisms of the multi-rotor targets. Hence, for the target recognition problem of multi-rotor drones, a multi-rotor target micro-Doppler parameter estimation method is proposed. Firstly, a signal frequency domain segmentation method is proposed based on the complex variational mode decomposition (CVMD) to separate the high-frequency part of the high-frequency flicker in the frequency domain. Secondly, for the signal after frequency domain segmentation, a flicker time domain position method based on singular value decomposition (SVD) is proposed. Finally, by integrating CVMD frequency domain segmentation and SVD time domain positioning, the reconstruction of multi-rotor target scintillation at different speeds is realized, and the micro-motion parameters of rotor blades are successfully estimated. The simulation results show that the method has high accuracy in estimating the micro-motion parameters of a multi-rotor, which makes up for the shortage of the traditional method in estimating the micro-motion parameters of the multi-rotor target.

End-to-End Recognition of Similar Space Cone–Cylinder Targets Based on Complex-Valued Coordinate Attention Networks

Except for the slight difference of micromotion parameters, some decoys and warheads have the same geometry and micromotion form. As a result, recognition of similar space cone–cylinder targets is one of the difficult problems in ballistic target recognition. In recent years, due to the good effect of deep neural networks (DNNs) in optical target recognition, space cone–cylinder target recognition methods based on DNN have attracted wide attention. However, these DNN-based methods only recognize the space cone–cylinder targets with different shapes and different micromotion forms. Moreover, these methods require some time-consuming preprocessing operations, which need to observe the target for at least one micromotion period. To recognize similar space cone–cylinder targets, we propose a complex-valued coordinate attention networks (CV-CANets)-based end-to-end recognition method. Firstly, we establish the signal model of space cone–cylinder targets. Secondly, we propose CV-CA blocks by transforming the coordinate attention mechanism into the complex-valued domain. Then, we construct CV-CANet based on the proposed CV-CA blocks. Finally, the proposed CV-CANet is trained and tested by the narrowband radar echo data, which is generated by electromagnetic calculation. Compared with the convolutional neural network (CNN)-based recognition methods, the proposed method can not only recognize the similar space cone–cylinder targets but also is superior in terms of time cost and observation requirement. Extensive experiments validate that the proposed recognition method is effective when the targets only have a slight difference on the precession angular frequency and the observation time is less than half a period.

Angle-micro-Doppler frequency image of underwater target multi-highlight combining with sparse reconstruction

Aiming at the problem of low angle resolution and difficult extraction of time-varying features in underwater target multi-highlight acoustic imaging, an acoustic imaging method of multi-highlight angle-micro-Doppler frequency for underwater target combining with sparse reconstruction is proposed by introducing sparse decomposition and time–frequency analysis into array signal processing. According to the difference between the underwater target highlight reflected wave modulation form caused by the micro-Doppler effect and time–frequency distribution, a kind of MicroDopplerlet time–frequency atom matching the complex modulation of the highlight reflection wave is designed. The multi-component signal processing is transformed into multiple single component signal processing problems by separating the highlight reflected wave from the echo by MP decomposition. In addition, a two-dimensional subspace processing method based on MP (MP-MUSIC) and a time–frequency distribution based on MP (MP-WVD) are constructed for sparsely reconstructed reflected wave of one single highlight, which are used to extract azimuth angle, pitch angle, micro-Doppler frequency information and acoustic image respectively. The proposed method has the advantages of noise robustness, high resolution and the ability to solve the underdetermined problem. The simulation results show that the proposed method can distinguish the multi-highlight structure of underwater target, and estimate the highlight angle, micro-Doppler frequency and micro-motion parameters of the target components correctly.

Development and validation of a modularized external fixator for generating standardized fracture healing micromotions in rats.

AimsTo fully verify the reliability and reproducibility of an experimental method in generating standardized micromotion for the rat femur fracture model.MethodsA modularized experimental device has been developed that allows rat models to be used instead of large animal models, with the aim of reducing systematic errors and time and money constraints on grouping. The bench test was used to determine the difference between the measured and set values of the micromotion produced by this device under different simulated loading weights. The displacement of the fixator under different loading conditions was measured by compression tests, which was used to simulate the unexpected micromotion caused by the rat’s ambulation. In vivo preliminary experiments with a small sample size were used to test the feasibility and effectiveness of the whole experimental scheme and surgical scheme.ResultsThe bench test showed that a weight loading < 500 g did not affect the operation of experimental device. The compression test demonstrated that the stiffness of the device was sufficient to keep the uncontrollable motion between fracture ends, resulting from the rat’s daily activities, within 1% strain. In vivo results on 15 rats prove that the device works reliably, without overburdening the experimental animals, and provides standardized micromotion reproductively at the fracture site according to the set parameters.ConclusionOur device was able to investigate the effect of micromotion parameters on fracture healing by generating standardized micromotion to small animal models.Cite this article: Bone Joint Res 2021;10(11):714–722.

Radar backscattering modelling and micro‐motion parameter estimation method for quadcopter

Due to the high-speed rotors and overlapped propellers, it is still a challenge to extract the micro-motion parameters of the quadcopter. In this work, the authors establish a radar backscattering model of the quadcopter based on the flight control theory and radar backscattering characteristics. Then, the improved template matching method and peak grouping method are proposed to capture the periodic information for overlapped propellers on a range-time profile and extract the rotation speeds of rotors. By simulating the nine types of quadcopter movements, the relative error of the rotation speeds is estimated to be lower than 1.4% for each rotor with a signal-to-noise ratio of 15 d B.

Micro-Motion Parameter Extraction for Ballistic Missile with Wideband Radar Using Improved Ensemble EMD Method

Micro-motion parameters extraction is crucial in recognizing ballistic missiles with a wideband radar. It is known that the phase-derived range (PDR) method can provide a sub-wavelength level accuracy. However, it is sensitive and unstable when the signal-to-noise ratio (SNR) is low. In this paper, an improved PDR method is proposed to reduce the impacts of low SNRs. First, the high range resolution profile (HRRP) is divided into a series of segments so that each segment contains a single scattering point. Then, the peak values of each segment are viewed as non-stationary signals, which are further decomposed into a series of intrinsic mode functions (IMFs) with different energy, using the ensemble empirical mode decomposition with the complementary adaptive noise (EEMDCAN) method. In the EEMDCAN decomposition, positive and negative adaptive noise pairs are added to each IMF layer to effectively eliminate the mode-mixing phenomenon that exists in the original empirical mode decomposition (EMD) method. An energy threshold is designed to select proper IMFs to reconstruct the envelop for high estimation accuracy and low noise effects. Finally, the least-square algorithm is used to do the ambiguous phases unwrapping to obtain the micro-curve, which can be further used to estimate the micro-motion parameters of the warhead. Simulation results show that the proposed method performs well with SNR at −5 dB with an accuracy level of sub-wavelength.

Rotational Doppler Detection of a Cone-shaped Target under the Illumination of a Vortex Electromagnetic Wave

The vortex ElectroMagnetic (EM) wave has a unique spiral phase wavefront. The rotational Doppler effect, induced by the lateral micromotion of a target is expected to provide a new mode of radar target detection. Under the illumination of vortex EM waves, the micromotion of a cone-shaped target periodically modulates the instantaneous frequency of the radar echo; this can effectively reflect the geometric characteristics and micromotion parameters of a cone-shaped target. This paper focuses on the estimation of cone-shaped target parameters under forward-looking radar conditions. First, based on the principle of vortex EM wave target rotational Doppler detection, a mathematical equation describing the vortex EM wave of a cone-shaped target echo is derived, and an echo rotational Doppler model is established. Second, a method for cone-shaped target parameter estimation under forward-looking conditions is proposed. Using the two-dimensional rotational Doppler information of the scattering points at the top and bottom of a cone-shaped target, the micromotion and geometric parameters of the target can be effectively estimated. The simulation results verify the effectiveness and robustness of the method proposed in this paper.

An mmWave Radar Based Real-Time Contactless Fitness Tracker Using Deep CNNs

“A healthy mind lives in a healthy body”. To maintain a healthy body, one has to follow a healthy lifestyle. Fitness trackers are excellent companions that help people meet their fitness goals and keep a healthy lifestyle. Thus, fitness tracking is becoming an integral part of the human lifestyle. But, most available fitness trackers nowadays require wearing/mounting on the body, which makes them unattractive for widespread use. To the contrary, mmwave radar sensors can accurately capture micro-motion parameters in a contactless configuration. In this paper, we propose and experimentally demonstrate the use of an mmwave radar sensor for real-time contactless fitness tracking using deep convolutional neural networks (CNNs). In this work, we created a setup to capture the body movements during various exercises by an mmwave radar. The radar data was processed and passed onto a deep CNN for training. Post-training results show that the system is able to classify different exercises in real-time with very high accuracy. The proposed system is computationally inexpensive and has the potential to become an effective alternative to body wearable fitness trackers for indoor fitness activities.

Micro‐motion parameter extraction of rotating target based on vortex electromagnetic wave radar

The vortex electromagnetic (EM) wave radar has the potential to obtain more accurate micro-motion parameters for target recognition. However, with the existing algorithms of micro-motion parameter extraction it is difficult to obtain the real rotation radius and tilt angle of a rotational target in the presence of multiple scattering points in the radar beam. A micro-motion parameter extraction algorithm for rotating targets based on the vortex EM wave radar is proposed in this article. The angular Doppler is obtained from the dual-mode vortex EM echoes. The time interval between the maximum and minimum angular Doppler frequency is derived. The relationship between the time interval and micro-motion parameters is shown. By combining the linear Doppler and the angular Doppler, the micro-motion parameters are roughly estimated. Then, fine micro-motion parameters are obtained by using an iterative soft threshold algorithm. The proposed algorithm can extract the real rotation radius and tilt angle in the case of multiple scattering points. The performance and robustness of the algorithm are proved by simulations.

Identification and micro-motion parameter estimation of non-cooperative UAV targets

With the wide application of unmanned aerial vehicles (UAV) in industrial production, transportation, and entertainment, it is urgent to identify UAVs in time. Traditional UAV recognition mainly depends on wireless communication, which puts forward high requirements for a communication environment and has no way to deal with non-cooperative targets. Therefore, it is urgent to explore a UAV target recognition scheme based on perception. In this paper, aiming at the time series preprocessing method, a coding-based sequence preprocessing method is proposed. This method effectively improves the effect of the Deep Learning method in the identification task. In order to verify the ability of Deep Learning in radar time series data processing and the effectiveness of the proposed method, the Deep Learning method is used to analyze the radar signal time series of the target to realize the target recognition. Finally,considering the influence of micro-motion factors on UAV targets, the neural network is used to estimate UAV’s micro-motion parameters to enhance the ability of target recognition with the help of micro-motion information.

基于动态散斑时频特性的合作目标微动参数反演

基于动态散斑时频特性的合作目标微动参数反演

GLRT Detection of Micromotion Targets for the Multichannel SAR-GMTI System

This letter investigates the micromotion target detection problem for the multichannel synthetic aperture radar (SAR)- ground moving target indication system. The multichannel SAR signal models of the micromotion target and the ground clutter in the raw data domain are established firstly. Then the generalized likelihood ratio test (GLRT) of the micromotion target is derived. Based on the analysis of the probability density functions of the test statistics, theoretical detection performance dependent on the micromotion parameters is provided. Simulated heterogeneous SAR data validate the effectiveness of the GLRT detector.

Bayesian sparse signal recovery based on log-Laplacian prior

This paper proposes a Bayesian sparse signal recovery algorithm. To improve performance on sparse representation, the log-Laplacian distribution is first defined. With a narrow main lobe and high tail values, it is used as a prior of the sparse signal to model the sparse characteristic. Note that analytical inference of the posterior of the sparse signal is a challenge, because the proposed log-Laplacian prior is not conjugate to the Gaussian likelihood. A maximum a posterior (MAP) estimation-based sparse signal recovery algorithm is further proposed. During the reconstruction of the sparse signal, MAP and maximum likelihood estimation are utilized to estimate the scaling parameter and noise variance, respectively, so as to avoid manual tuning of parameters. Additionally, with the use of the conjugate-gradient algorithm, large matrix inversion is avoided and computational efficiency is improved. Experimental results based on both simulated and measured data validate the effectiveness of the proposed log-Laplacian prior-based sparse signal recovery algorithm. Further, it is applied to micromotion parameters estimation and inverse synthetic aperture radar imaging to confirm its validity.