Analysis of echo signal modulation characteristic parameters on aerial and space targets

Based on the scattering characteristic,the comparison of RCS(radar cross-section)at different positions of a target in the same direction of incidence can be obtained first by extruding or deleting part of the entity.A simulation method of aerial&space targets echo characteristics(A&STEC)is proposed that is universal to aerial and space targets.We utilize a fixed-wing UAV(unmanned aerial vehicle)and typical missiles in simulation.The echo signal modulation characteristic parameters are calculated theoretically by the atmospheric attenuation model,the finite element method and a MUMPS solver.The verification simulations show that this method can analyze the influence of the target shape,incident direction,detection position and detection frequency on echo waveform,intensity and energy distribution.The results show that the profile of echo waveform can invert the general shape of the target.The relationship between time and intensity can determine whether the target is moving towards or away from the detector in addition.These conclusions can provide a reference for the ballistic missile target tracking and the defense against UVA intrusion in theory.

SIMULATION STUDY ON AIRBORNE SAR ECHO SIGNAL

Through analyzing the influence on echo signal by factors of kinematical parameters of airborne SAR platform and radar antenna direction, this letter, on the basis of classical SAR echo signal analogue algorithm, puts forward certain airborne SAR echo signal analogue algorithm of distance directional frequency domain pulse coherent accumulation, and goes through simulation. The simulation results have proved the effectiveness of this algorithm.

Detection and correction of level echo based on generalized S-transform and singular value decomposition

The echo of the material level is non-stationary and contains many singularities.The echo contains false echoes and noise,which affects the detection of the material level signals,resulting in low accuracy of material level measurement.A new method for detecting and correcting the material level signal is proposed,which is based on the generalized S-transform and singular value decomposition(GST-SVD).In this project,the change of material level is regarded as the low speed moving target.First,the generalized S-transform is performed on the echo signals.During the transformation process,the variation trend of window of the generalized S-transform is adjusted according to the frequency distribution characteristics of the material level echo signal,achieving the purpose of detecting the signal.Secondly,the SVD is used to reconstruct the time-frequency coefficient matrix.At last,the reconstructed time-frequency matrix performs an inverse transform.The experimental results show that the method can accurately detect the material level echo signal,and it can reserve the detailed characteristics of the signal while suppressing the noise,and reduce the false echo interference.Compared with other methods,the material level measurement error does not exceed 4.01%,and the material level measurement accuracy can reach 0.40%F.S.

Ultrasonic echo denoising in liquid density measurement based on improved variational mode decomposition

The ultrasonic echo in liquid density measurement often suffers noise,which makes it difficult to obtain the useful echo waveform,resulting in low accuracy of density measurement.A denoising method based on improved variational mode decomposition(VMD)for noise echo signals is proposed.The number of decomposition layers of the traditional VMD is hard to determine,therefore,the center frequency similarity factor is firstly constructed and used as the judgment criterion to select the number of VMD decomposition layers adaptively;Secondly,VMD algorithm is used to decompose the echo signal into several modal components with a single modal component,and the useful echo components are extracted based on the features of the ultrasonic emission signal;Finally,the liquid density is calculated by extracting the amplitude and time of the echo from the modal components.The simulation results show that using the improved VMD to decompose the echo signal not only can improve the signal-to-noise ratio of the echo signal to 20.64 dB,but also can accurately obtain the echo information such as time and amplitude.Compared with the ensemble empirical mode decomposition(EEMD),this method effectively suppresses the modal aliasing,keeps the details of the signal to the maximum extent while suppressing noise,and improves the accuracy of the liquid density measurement.The density measurement accuracy can reach 0.21%of full scale.

A Raw Echo Simulator for the C-SAR Ground Demonstration System

he raw echo simulator, one of the fundamental components of Chinas Cband spaceborne Synthetic Aperture Rador(SAR) ground demonstration system is presented in this paper. The function of the raw echo simulator is to provide realistic radar return in real time, and form a closed loop system together with the onboard radar equipment and the signal processor. It can be used to verify the performance of the SAR system and imaging algorithm before practical launching. The computer simulation models for the raw echo simulator are analyzed, and some preliminary results of the simulated experiments are given.

Assessment the Optimal Effect of Time of Repetition: Extrinsic Pulse Parameter on Gd-DTPA Enhanced, Spin-Echo T1-Weighted MR Images under Low Magnetic Field Strength

The contrast agent concentration, the time of repetition (TR) and magnetic field strength are significant parameters that influence for the accurate signal intensity (SI) in quantitative Magnetic Resonance Imaging (MRI). Therefore, this study was conducted to investigate and refine the dependence and the optimal effect of Time of Repetition (TR) on the relationship between signal intensity and Gd-DTPA (Gadolinium-diethylene-triaminepenta-acetic acid) concentration, after applying two-dimensional (2D) Spin Echo (SE) pulse sequence under low-field MRI. In addition to that, the optimal concentration of Gd-DTPA at given sequence parameters at low-field MRI was also evaluated. A water-filled phantom was constructed for a range of Gd-DTPA concentrations (0 - 6 mmol/L) and the mean signal intensities (SIs) were assessed in the defined region of interest on T1-weighted images with different TR values (40 - 2000 ms). The generated signal-concentration curves for Gd-DTPA revealed that increasing TR was associated with the increase of the overall SIs and the maximum relationship between SI to concentration. Moreover, the required Gd-DTPA concentration to produce the maximum SI was associated to decrease with the increase of TR. In addition to this, the application of beyond 100 ms TR values in this study with relatively higher concentrations (beyond 1 - 2 mmol/L) has resulted predominantly non-linear patterns in the signal-concentration curves and it appears the saturation or decay of the SIs due to T2 effect. From these results, it can be suggested that the selection of relatively lower Gd-DTPA concentration ( mmol/L) with less than 800 ms (<800 ms) TR values can produce a better linear relationship between the concertation and SIs in T1-weighted SE low field contrast-enhanced MRI. Furthermore, this study also outlined the significance and necessity of the optimization of TR in SE sequence in low field MRI prior to a particular examination.

基于激光雷达回波信号的雾天船舶航线快速辨识

激光雷达所处环境噪声较大,船舶航线辨识困难,为解决该问题,提出基于激光雷达回波信号的雾天船舶航线快速辨识方法。采用信号累积增强算法增强雾天激光雷达回波信号,利用改进经验模态分解算法去噪处理增强后的回波信号,结合回波信号通过最小类内方差获取得到船舶航线和水面最佳分割阈值,提取船舶航线特征点。利用最小二乘法拟合法拟合船舶航线特征点,完成雾天船舶航线辨识。实验结果表明,所提方法应用后激光雷达回波信号更平滑,输出信噪比更高,均方根误差更低,船舶航线辨识准确率更理想。

基于分裂基FFT的S波段雷达信号处理单元设计与实现

回波信号的信息解调主要依赖于FFT完成,本文提出了一种基于分裂基FFT的FPGA实现信号处理单元方案,用于高效解调回波信号信息。在原方案中,距离维解调由DSP完成,速度维解调在PC端进行,但其采用的单核DSP无法同时处理距离维解调与数据上传,导致实时性较低。本文利用FPGA并行处理和灵活可编程的特点,将两次信号解调集成至FPGA中实现,并完成了数据处理与上传的并行设计,提高了数据处理的实时性与可靠性。其中分裂基FFT是实现上述功能的关键,本文通过设计自适应计算单元与数据流控制单元,提升了分裂基FFT的处理速度,相比传统结构,本文方案节省了5.4%以上的计算周期;相比改进后的分裂基FFT,可节省2.31%以上的计算周期。闭环测试结果表明,本文设计的集成化信号处理单元解调得到的目标点信噪比高达70 dB,能够很好地满足信号处理的要求。

裂纹对弹性波在混凝土中传播特性的影响研究

目的为研究混凝土中裂纹对弹性波传播特性的影响,方法采用COMSOL软件构建弹性波在不同形态混凝土裂纹中的传播数值模型,并开展带裂纹混凝土的弹性波传播试验,系统研究混凝土裂纹特征(裂纹角度、裂纹深度、裂纹距声发射源距离、裂纹数量等)与弹性波传播之间的关系。结果研究发现:数值模拟结果与试验现象吻合较好,验证了数值模型的可靠性。其中,裂纹数量和裂纹距声发射源距离的变化对回波峰值影响较大,距声发射源2 cm裂纹的回波峰值最大,为3.46×10^(6)Pa;对波速和信号损伤影响最大的因素为裂纹深度,60 mm深度裂纹损伤率高达62.14%、波速衰减至2590.674 m/s,其次为裂纹数量,信号损伤率达31.28%。基于不同裂纹特征的波速变化规律,对裂纹角度和深度的波速关系模型进行回归分析,回归曲线拟合优度较好,R^(2)均大于0.95。结论混凝土中存在的裂纹和裂纹特征对弹性波传播具有显著影响,本文研究结果为混凝土无损检测技术提供了理论支撑。

基于激光雷达回波信号的雾天车道线快速检测

当前主要通过深度神经网络模型提取路面车道线,并设计能见度检测网络,根据车道线可见长度检测路面车道线。但是,在雾天,基于深度分割神经网络设计编码解码结构非相关因素过多,无法通过其提取车道线特征图,无法准确检测图像坐标系下可见车道线的高度。针对雾天驾驶时的视觉障碍问题,以激光雷达技术为支撑,提出雾天车道线快速检测方法。根据激光雷达回波信号中每个回波脉冲宽度级的扫描点数,采用最小类内方差算法,阈值分割路面与车道线扫描点,由3σ准则分离出车道线的种子点后,基于高斯核函数加权搜索的生长准则,经区域生长得到完整的车道线种子点集。基于密度的空间聚类算法二次聚类获取的车道线种子点集,得到车道线的识别结果。以识别结果为基础,建立抛物线模型,结合随机采样一致性算法和最小二乘法,依据拟合分值迭代取得最优模型,通过拟合完成车道线检测。实验结果表明:该方法屏蔽雾天干扰引起的非相关因素,清晰检测出雾天环境中的多种车道线。在雾天环境车道线检测中,交并比高于0.95,F1值高于96%,可以满足准确性和实时性需求,为雾天驾驶提供有效的解决方案。

Diffusion tensor imaging of spinal microstructure in healthy adults： improved resolution with the readout segmentation of long variable echo-trains

Diffusion tensor imaging plays an important role in the accurate diagnosis and prognosis of spinal cord diseases. However, because of technical limitations, the imaging sequences used in this technique cannot reveal the fine structure of the spinal cord with precision. We used the readout segmentation of long variable echo-trains（RESOLVE） sequence in this cross-sectional study of 45 healthy volunteers aged 20 to 63 years. We found that the RESOLVE sequence significantly increased the resolution of the diffusion images and improved the median signal-to-noise ratio of the middle（C4–6） and lower（C7–T1） cervical segments to the level of the upper cervical segment. In addition, the values of fractional anisotropy and radial diffusivity were significantly higher in white matter than in gray matter. Our study verified that the RESOLVE sequence could improve resolution of diffusion tensor imaging in clinical applications and provide accurate baseline data for the diagnosis and treatment of cervical spinal cord diseases.

三维多回波聚合梯度回波序列在腰骶丛成像中的初步应用

目的 探讨3D多回波聚合梯度回波序列(3D GEMEC)在腰骶丛神经成像中的应用价值。方法 回顾性分析2022年8~9月六安市人民医院以腰腿疼为主诉的32例住院患者的影像学资料,男15例,女17例,年龄28~78(60.1±15.8)岁。在不同1.5T平台上分别完成腰骶丛3D GEMEC序列和三维T2加权快速梯度回波(3D T2 FFE)序列,比较两种序列腰骶丛神经的信噪比、对比噪声比和主观显示效果。结果 3D GEMEC序列腰骶丛神经的信噪比、对比噪声比以及主观显示效果与3D T2 FFE序列的差异均无统计学意义(P=0.250、0.146、0.083)。结论 3D GEMEC序列可以清晰地显示腰骶丛神经的解剖结构并抑制背景信号,在腰骶丛神经病变的诊断方面具有一定的临床应用前景。

垂直大气湍流影响下的天气雷达回波信号优化检测方法

垂直大气湍流导致回波信号在大气中扩散,使回波信号质量较差,极易被噪声所淹没,因此,提出垂直大气湍流影响下的天气雷达回波信号优化检测方法。通过分析垂直大气湍流对天气雷达回波信号传输特性的影响,匹配滤波优化算法,改善垂直大气湍流影响导致质量下降的回波信号,利用模糊逻辑方法提取回波特征参数并构建对应隶属函数,设定阈值,获取天气雷达回波信号检测结果。实验结果表明:该方法可有效检测不同仰角的天气雷达回波信号,回波信号波形均匀且波动幅度小,检测效果较好。

基于MI-EMD的激光引信回波信号去噪方法研究

为解决激光引信回波信号容易被噪声污染、信噪比低的问题,提出一种基于互信息经验模态分解的激光引信回波信号去噪方法。该方法融合了互信息相关性和经验模态分解自适应性的特点,对基于雷达原理建立的激光引信回波信号进行经验模态分解,通过互信息及相关阈值区分噪声模态和信号模态,提取出分解信号中的有用信号分量,并将其相关模态进行重构实现噪声的有效去除。实验结果表明:该方法处理后的信噪比提高到了18.8854 dB,均方根误差减小到2.81×10^(-6),且去噪后信号曲线的平滑度较高。该方法能有效滤除激光引信回波信号中的噪声,很好地还原激光引信回波原始信号,保证信号的完整性,为后续激光引信在噪声条件下的精确定距奠定了坚实的基础。

用于土壤温湿度检测的声表面波标签设计

针对土壤温湿度的并行检测提出了基于声表面波标签的解决方案。采用耦合模理论对阻抗负载型声表面波标签进行建模与仿真,得到负载反射栅回波幅值和相位随电容值变化规律。在此基础上,根据声表面波标签对土壤温湿度不同的敏感机理,设计了一种双通道双边带标签结构,采用时分多址的方法解决了对不同深度层级土壤同时检测时存在的回波信号混叠问题。通过回波信号的相位和幅值比实现了土壤温湿度检测,结合矢量网络分析仪和阅读器对标签进行测试,测试结果表明了标签的有效性。

异步传输模式下电路探测激光回波信号增强

激光回波信号是电路探测传输的常用信号,信号强度是保障集成电路探测效果的关键,因此提出大数据异步传输模式下的电路探测激光回波信号增强方法。利用多权重混合分布模型消除激光回波信号的背景噪声;根据异步传输模式的通信方式,建立激光回波信号接收机,接收保留完整信号信元;利用整形脉冲信号处理方法完成信号累积和自适应匹配滤波,实现大数据异步传输模式下电路探测激光回波信号的增强。实验结果表明,所提方法的信号增强结果在任意波长处的能量和光谱强度均较高。

基于非负矩阵分解的双曲调频信号目标回波增强

双曲调频信号是主动声呐中常用的多普勒不敏感信号。在实际使用中,由于信道变化和混响影响,目标回波较弱。针对水下航行器主动声呐探测性能下降问题,本文提出基于非负矩阵分解的双曲调频信号目标回波增强方法。该方法对目标回波和干扰信号的基矩阵分别定义,将发射信号的时频图作为目标回波的频率信息矩阵,对目标回波的时间信息矩阵做出约束,迭代处理完成目标回波的增强,获得较高增益的匹配回波检测结果。数值仿真和海上试验结果表明:本文方法与传统匹配滤波方法相比,其峰均比值提高了2 dB以上,提高了回波检测结果。

基于人工智能的多模态雷达自适应抗干扰优化算法

多模态雷达系统容易受到外界环境干扰,如天气条件、电磁干扰等,而这些干扰可能会影响多模态雷达数据的准确性和稳定性。多模态雷达的抗干扰性能决定雷达的测量精度,因此,为提升多模态雷达的抗干扰能力,提出基于人工智能的多模态雷达自适应抗干扰优化算法。该算法以多模态雷达信号模型为基础,分析距离速度同步欺骗干扰、频谱弥散干扰原理,计算欺骗干扰时雷达接收的总回波信号。将计算的回波信号结果输入至人工智能的YOLOv5s深度学习模型中,通过模型的训练和映射处理,完成多模态雷达自适应抗干扰优化,实现雷达欺骗性信号干扰抑制。测试结果显示,该算法的干扰对消比结果在0.935以上,干扰输出功率结果在0.017以下,能够可靠完成多干扰和单一干扰两种干扰抑制,实现多模态雷达自适应抗干扰优化。

基于超声时频分析与残差网络的生物组织变性识别

为了能对高强度聚焦超声(HIFU)治疗过程中的生物组织进行实时准确的变性识别,提出了一种基于超声时频分析与残差网络(ResNet)的生物组织变性识别方法。首先,采用广义S变换(GST)方法对生物组织超声回波信号进行时频分析,得到二维时频图;然后,通过迁移学习,将在ImageNet数据集上训练得到的参数应用于超声回波信号数据集;最后,利用ResNet101模型从生物组织变性前后的时频图中学习和提取有效的变性信息,并可视化变性特征轨迹,实时地完成生物组织变性识别。实验结果表明:相较于现有基于信号能量,AR系数以及熵特征的变性识别方法,GST-ResNet方法无需人为经验选取特征参数,具有更高的识别率,可以实时准确地完成生物组织的变性识别。

复杂环境中海上通信局部通信链路中断预测

由于海上环境复杂且易受到日凌现象的影响,通信链路容易中断。为了及时恢复正常通信,提出复杂环境中海上通信局部通信链路中断预测方法。分析复杂环境中海上局部通信链路中断原因,构建基于波浪滑翔器的通信链路中断预测框架,在该框架中计算波浪滑翔器链路中断回波时间,确定中断回波信号所在的位置,从而得到海上通信局部通信链路中断预测结果。由实验结果可知,该方法预测在暴雨环境的1.0~1.3 s、2.0~2.7 s、暴风环境的2.0~2.5 s出现接收信号丢失情况,与实际情况一致,说明该方法的预测结果精准。