Automatic recognition of sonar targets using feature selection in micro-Doppler signature

Currently,the use of intelligent systems for the automatic recognition of targets in the fields of defence and military has increased significantly.The primary advantage of these systems is that they do not need human participation in target recognition processes.This paper uses the particle swarm optimization(PSO)algorithm to select the optimal features in the micro-Doppler signature of sonar targets.The microDoppler effect is referred to amplitude/phase modulation on the received signal by rotating parts of a target such as propellers.Since different targets'geometric and physical properties are not the same,their micro-Doppler signature is different.This Inconsistency can be considered a practical issue(especially in the frequency domain)for sonar target recognition.Despite using 128-point fast Fourier transform(FFT)for the feature extraction step,not all extracted features contain helpful information.As a result,PSO selects the most optimum and valuable features.To evaluate the micro-Doppler signature of sonar targets and the effect of feature selection on sonar target recognition,the simplest and most popular machine learning algorithm,k-nearest neighbor(k-NN),is used,which is called k-PSO in this paper because of the use of PSO for feature selection.The parameters measured are the correct recognition rate,reliability rate,and processing time.The simulation results show that k-PSO achieved a 100%correct recognition rate and reliability rate at 19.35 s when using simulated data at a 15 dB signal-tonoise ratio(SNR)angle of 40°.Also,for the experimental dataset obtained from the cavitation tunnel,the correct recognition rate is 98.26%,and the reliability rate is 99.69%at 18.46s.Therefore,the k-PSO has an encouraging performance in automatically recognizing sonar targets when using experimental datasets and for real-world use.

Micro-Doppler feature extraction of micro-rotor UAV under the background of low SNR

Micro-Doppler feature extraction of unmanned aerial vehicles(UAVs)is important for their identification and classification.Noise and the motion state of the UAV are the main factors that may affect feature extraction and estimation precision of the micro-motion parameters.The spectrum of UAV echoes is reconstructed to strengthen the micro-motion feature and reduce the influence of the noise on the condition of low signal to noise ratio(SNR).Then considering the rotor rate variance of UAV in the complex motion state,the cepstrum method is improved to extract the rotation rate of the UAV,and the blade length can be intensively estimated.The experiment results for the simulation data and measured data show that the reconstruction of the spectrum for the UAV echoes is helpful and the relative mean square root error of the rotating speed and blade length estimated by the proposed method can be improved.However,the computation complexity is higher and the heavier computation burden is required.

Parity recognition of blade number and manoeuvre intention classification algorithm of rotor target based on micro-Doppler features using CNN

This paper proposes a parity recognition of blade number and manoeuvre intention classification algorithm of rotor target based on the convolutional neural network(CNN) using micro Doppler features. Firstly, the time-frequency spectrograms are acquired from the radar echo by the short-time Fourier transform.Secondly, based on the obtained spectrograms, a seven-layer CNN architecture is built to recognize the blade-number parity and classify the manoeuvre intention of the rotor target. The constructed architecture contains a leaky rectified linear unit and a dropout layer to accelerate the convergence of the architecture and avoid over-fitting. Finally, the spectrograms of the datasets are divided into three different ratios, i.e., 20%, 33% and 50%,and the cross validation is used to verify the effectiveness of the constructed CNN architecture. Simulation results show that, on the one hand, as the ratio of training data increases, the recognition accuracy of parity and manoeuvre intention is improved at the same signal-to-noise ratio(SNR);on the other hand, the proposed algorithm also has a strong robustness: the accuracy can still reach 90.72% with an SNR of – 6 dB.

Modeling simulation and experiment of micro-Doppler signature of precession

Spatial precession is a special micro-motion of the spinning-directional target, and the micro-Doppler signature of the cone-shaped target with precession is studied. The micro-motion model of precession is built first, and then the micro-Doppler model is developed based on the proposed concept of micro-motion ma- trix, by which the theoretical formula of micro-Doppler signature of precession is derived. In order to further approach to the actual case, the occlusion effect is firstly considered in micro-Doppler, and the simulated result with occlusion effect is well in accordance with the measured result in microwave anechoic chamber, which suggests that the micro-motion model and micro-Doppler model of precession are both valid.

Parameter estimation for rigid body after micro-Doppler removal based on L-statistics in the radar analysis

In traditional inverse synthetic aperture radar （ISAR） imaging of moving targets with rotational parts, the micro-Doppler （m-D） effects caused by the rotational parts influence the quality of the radar images. Recently, L. Stankovic proposed an m-D removal method based on L-statistics, which has been proved effective and simple. The algorithm can extract the m-D effects according to different behaviors of signals induced by rotational parts and rigid bodies in time-frequency （T-F） domain. However, by removing m-D effects, some useful short time Fourier transform （STFT） samples of rigid bodies are also extracted, which induces the side lobe problem of rigid bodies. A parameter estimation method for rigid bodies after m-D removal is proposed, which can accurately re- cover rigid bodies and avoid the side lobe problem by only using m-D removal. Simulations are given to validate the effectiveness of the proposed method.

DETECTION ON MICRO-DOPPLER EFFECT BASED ON LASER COHERENT RADAR

A laser coherent detection system of 1550 nm wavelength was presented, and experimen- tal research on detecting micro-Doppler effect in a dynamic target was developed. In the study, the return signal in the time domain is decomposed into a set of components in different wavelet scales by multi-resolution wavelet analysis, and the components are associated with the vibrational motions in a target. Then micro-Doppler signatures are extracted by applying the reconstruction. During the course of the final data processing frequency analysis and time-frequency analysis are applied to analyze the vibrationM signals and estimate the motion parameters successfully. The experimental results indicate that the system can effectively detect micro-Doppler information in a moving target, and the tiny vibrational signatures also can be acquired effectively by wavelet multi-resolution analy- sis and time-frequency analysis.

Particle swarm optimization for rigid body reconstruction after micro-Doppler removal in radar analysis

The rotating micro-motion parts produce micro-Doppler(m-D)effects which severely influence the quality of inverse synthetic aperture radar(ISAR)imaging for complex moving targets.Recently,a method based on short-time Fourier transform(STFT)and L-statistics to remove m-D effects is proposed,which can separate the rigid body parts from interferences introduced by rotating parts.However,during the procedure of removing m-D parts,the useful data of the rigid body parts are also removed together with the m-D interferences.After summing the rest STFT samples,the result will be affected.A novel method is proposed to recover the missing values of the rigid body parts by the particle swarm optimization(PSO)algorithm.For PSO,each particle corresponds to a possible phase estimation of the missing values.The best particle is selected which has the minimal energy of the side lobes according to the best fitness value of particles.The simulation and measured data results demonstrate the effectiveness of the proposed method.

Micro-Doppler Parameter Estimation Method Based on Compressed Sensing

A micro-Doppler parameter estimation method based on compressed sensing theory is proposed in this paper.The micro-Doppler parameter estimation algorithm was improved for micro-motion targets with translation in this paper.Relatively ideal micro-Doppler parameter estimation results were obtained.The proposed micro-Doppler parameter estimation was compared with the traditional micro-Doppler parameter estimation algorithm.Requirements for return signal length were analyzed with this new algorithm and its performance was also analyzed in various environments with different SNR.

Micro-Doppler effect testing technique for attitude of projectile in space flight

To measure projectile attitude in space flight, based on continuous wave （CW） radar, a new micro-Doppler effect testing technique is developed in this paper. It also establishes radar testing model for attitude of flying projectile and resolve micro-Doppler effect of projectile motion attitude. By distinguishing and geting attitude parameters such as micro-motion period, this technique can in- tuitively estimate the flight stability of projectile, and the validity of this technique is proved accord- ing to flight tests.

Convex Optimization-Based Rotation Parameter Estimation Using Micro-Doppler

We present a novel algorithm that can determine rotation-related parameters of a target using FMCW （frequency modulated continuous wave） radars, not utilizing inertia information of the target. More specifically, the proposed algorithm estimates the angular velocity vector of a target as a function of time, as well as the distances of scattering points in the wing tip from the rotation axis, just by analyzing Doppler spectrograms obtained from three or more radars. The obtained parameter values will be useful to classify targets such as hostile warheads or missiles for real-time operation, or to analyze the trajectory of targets under test for the instrumentation radar operation. The proposed algorithm is based on the convex optimization to obtain the rotation-related parameters. The performance of the proposed algorithm is assessed through Monte Carlo simulations. Estimation performance of the proposed algorithm depends on the target and radar geometry and improves as the number of iterations of the convex optimization steps increases.

基于最近邻域的弹道多目标分辨及micro-Doppler提取

针对尺寸相近的弹道多目标分辨及micro-Doppler提取问题,文中提出了一种新的基于"选择"的弹道多目标分辨及micro-Doppler提取方法.在滑动散射模型的基础上,根据弹道目标micro-Doppler变化率的变化趋势,首先对时频谱中各点的最近邻域进行多尺度编码,通过分层处理实现多目标的分辨,然后对各分层区域进行方位编码序列配准,运用零点回溯的方法提取出目标的micro-Doppler信息.仿真结果表明,该方法能够有效地克服多散射点模型交叉区域对多目标分辨及曲线分离的干扰作用,适用于多种微动目标散射模型,且抗噪性好,较好地实现了弹道多目标分辨及micro-Doppler提取.

Multiple walking human recognition based on radar micro-Doppler signatures

The recognition of human movements based on radar m-D(micro-Doppler) signatures attracts great interest in the field of radar research on automatic target recognition. Because there are multiple frequency components overlapping seriously in the radar echoes from walking humans, it is a very difficult work to recognize walking humans based on radar echoes. In this paper, a recognition method of walking humans based on radar m-D signatures is proposed. In this method, the m-D spectrum is generated by generalized S transform first,and then the entropy segmentation is used to segment the interesting region from the original spectrum. Next,the m-D features are extracted from the m-D region. Lastly, the support vector machine is used to recognize different walking human targets. The simulation experiments considering two factors of height and velocity are also conducted to test the performance of this proposed method.

微多普勒辅助的城市环境无人机编队检测方法

针对城市复杂环境下电磁环境复杂、多径杂波和干扰信号密集等现象,传统的无人机(UAV)检测方法通过获取回波信号提取目标多普勒信息进行检测,易受到环境影响导致检测效果不理想。该文提出微多普勒辅助的城市环境无人机编队检测方法,充分利用无人机的微动特征,能够在复杂环境下提高检测精度。首先,参数化建模表征城市复杂环境下无人机旋翼的雷达回波微多普勒信号,利用YOLOv5s检测微多普勒闪烁脉冲,有效提取位置信息;然后,引入雷达信号分选方法的脉冲重复间隔(PRI)变换,分类获得无人机编队数量;最后,利用Kmeans算法验证无人机编队检测方法的准确性。结果表明,所提方法在信噪比2 dB时7架无人机的检测精度高于90%,能够用于城市复杂环境存在干扰脉冲、多径效应、局部脉冲丢失的无人机编队检测。

基于微多普勒角点特征与Non-Local机制的穿墙雷达人体步态异常终止行为辨识技术

穿墙雷达能够穿透建筑物墙体,实现室内人体目标探测。利用深度学习提取不同肢节点的微多普勒特征,可以有效辨识障碍物后的人体行为。但是,当生成训练、验证集与生成测试集的受试者不同时,基于深度学习的行为识别方法测试准确率相对验证准确率往往较低,泛化能力较差。因此,该文提出一种基于微多普勒角点特征与Non-Local机制的穿墙雷达人体步态异常终止行为辨识技术。该方法利用Harris与Moravec检测器提取雷达图像上的角点特征,建立角点特征数据集;利用多链路并行卷积和Non-Local机制构建全局上下文信息提取网络,学习图像像素的全局分布特征;将全局上下文信息提取网络重复堆叠4次得到角点语义特征图,经多层感知机输出行为预测概率。仿真和实测结果表明,所提方法可以有效识别室内人体步行过程中存在的坐卧、跌倒等突发步态异常终止行为,在提升识别准确率、鲁棒性的前提下,有效控制泛化精度误差不超过6.4%。

急性粒-单核细胞白血病患者造血干细胞移植后微血栓形成凝血指标监测及治疗1例报告

本文报道1例急性粒-单核细胞白血病(急性髓系白血病M4型)(CBFB基因阳性)患者,经亲缘半相合造血干细胞移植后并发下肢微血栓形成,并总结其诊治经验。该患者经常规化疗后获完全缓解,但又于2年后复发,遂予行亲缘半相合造血干细胞移植。在患者接受移植后的常规治疗期间,动态观察其凝血相关指标[包括D-二聚体(D-dimer, D-D)、抗凝血酶Ⅲ(antithrombinⅢ,AT-Ⅲ)、血管性血友病因子(von Willebrand factor, vWF)、纤维蛋白原降解产物(fibrinogen degradation products, FDP)、纤维蛋白单体(fibrin monomer, FM)]。在移植后100 d左右,患者出现黄疸及转氨酶升高,采用经右下肢股静脉置管行血浆置换治疗3次。在去除静脉置管1周后,患者出现左踝部指凹性水肿,血管B超检查提示其左下肢腓肠肌浅静脉丛有微血栓形成;凝血指标检测显示,D-D、vWF、FDP、FM等指标升高,AT-Ⅲ水平降低。予口服拜瑞妥5 mg/d抗栓治疗2周后,患者上述凝血指标渐趋正常,左下肢肌间静脉丛血栓和肢肿消失。在造血干细胞移植后,除密切检查血管B超,动态监测D-D、AT-Ⅲ、vWF、FDP、FM等凝血指标可预测移植相关微血栓和高凝状态,并为临床开展有效抗的凝治疗提供实验依据。

复合微多普勒信号的时频谐波分析方法

在对复合微动目标的雷达回波进行时频分析时,存在时频曲线能量难以聚焦等问题,影响目标微多普勒特征提取和分析。对此,提出了时频谐波条纹的概念以及相应时频变换方法。基于复合微动包含中心运动分量和高频运动分量的假设,首先利用样条函数拟合中心分量频率曲线,构造时频变换核函数。然后,通过参数化时频变换将时域的复合微多普勒信号转化为时频平面上的谐波条纹。所提方法相比短时傅里叶变换和伪魏格纳分布等经典方法,能够明显改善复合微动目标雷达回波时频变换的能量聚集性与杂散抑制效果,仿真验证了所提方法的有效性和对低信噪比条件的适应性。

低脉冲重复频率条件下无人机微动参数提取

针对低脉冲重复频率条件下的无人机微动特征提取问题,提出一种基于原子放缩正交匹配追踪(orthogonal matching pursuit,OMP)分解的微动参数估计方法。首先,通过计算时频谱熵从无人机回波数据中筛选出具有显著微动特征的信号。其次,采用变分模态分解(variational modal decomposition,VMD)算法提取旋翼叶片的旋转频率,提出了基于原子放缩的OMP分解方法实现了对无人机旋翼叶片长度的估计。仿真实验表明所提方法相比于传统的OMP方法和VMD-OMP方法都具有明显优势。最后,采用实测数据验证了所提算法的有效性。

基于半波相消的旋转叶片微动回波时频分析

在雷达探测场景中常见旋转引起的微多普勒效应,产生的微动特征与目标的几何和运动参数相关。文中从微动回波的线积分模型出发,基于半波相消的角度对旋转叶片微动回波时频特征形成机理进行了全面分析。首先在远场条件下,基于半波相消分析了旋转微动瞬时频率的形成机理,解释了其旋转微动时频特征的双端效应。进一步地,将半波相消分析扩展至近场条件,解释了近场条件下时频图双端区和镜面散射区凸显的效应。对于远场和近场条件,该文均给出了时频结果的定量解析计算方法。从图像形状和能量变化的角度对近、远场的时频特征进行分析,讨论了不同条件和参数对时频特征形状和细节的影响,基于半波相消分析将近场和远场分析统一起来,同时将线积分模型和局部散射模型联系起来。仿真实验验证了所提分析方法的正确性。

运动目标的多维度微运动特征提取研究

微多普勒特征提取作为一种常用的时频分析工具,对微动目标特征的提取重构具有重要意义.为了更好地研究多运动的微多普勒效应,提出了一种运动姿态分类方法.按照目标频移是否随时间变化可以将运动姿态分为频移时变运动和频移时不变运动.频移时变运动包括平移、翻滚和振动.针对这种运动应分析对比不同时间对应的瞬态频移,频移时不变运动主要为旋转运动.本文通过微多普勒效应理论结合电磁波频域模型,实现3D运动目标微动特性提取的仿真建立目标,分析不同环境条件例如晴天阴天、有无湍流对探测的影响,为后续实验研究奠定理论基础.开展基于收发同置系统的多特征运动目标的微多普勒频移探测实验,实验结果表明,不同目标位置上频移的幅度、正负性和谱线宽度旨在反演目标形状、运动姿态、运动方向和速度.利用FFTshift函数对一维数据进行解调分析,实现三维时间-频率-强度关系的研究.本研究实现了对目标宏观形状特性的测量以及微观运动信息的提取,为雷达探测和识别奠定基础.

基于变分模态分解与优选的超高分辨ISAR成像微多普勒抑制方法

逆合成孔径雷达(ISAR)在对空中目标成像时,目标自身的转动、振动等局部微动将产生微多普勒效应,回波将附加额外的多普勒调制,造成频谱展宽。在超高分辨条件下,这一微动特性将会影响主体散射点的聚焦,导致目标图像局部散焦模糊,严重影响成像质量。并且,微多普勒相位还具有时变非平稳特性,难以从ISAR目标回波中准确估计或分离出微多普勒。为了解决上述问题,该文利用目标主体回波和微多普勒分量的时频分布差异,提出一种基于变分模态分解(VMD)与优选的非参数化方法抑制了回波中的微多普勒分量,消除了微多普勒对成像的影响,获得超高分辨率的无人机ISAR成像结果。该文首先引入VMD算法并将其扩展到复数域,将ISAR目标回波数据沿方位向分解为若干个中心频率均匀分布于多普勒采样带宽中的模函数,在此基础上利用图像熵指标优化分解参数和筛选成像模态,以保证微多普勒的良好抑制和主体回波的较完整保留。与现有基于经验模态分解(EMD)和局部均值分解(LMD)的方法相比,所提方法在超大带宽条件下对旋翼微动引起的微多普勒干扰有着更为出色的抑制效果,而且对机身部分的保留更为完整。最后,通过仿真对比和超宽带微波光子ISAR无人机实测数据处理,证明了该文所提方法的有效性和优势。