220kV线路工程滑坡地质灾害危险性分析

文章介绍了喜德县红莫升压站-鲁基升压站220kV线路工程滑坡地质灾害危险性现状评估和预测评估,确定了评估区内滑坡地质灾害对工程的影响,以及工程建设加剧已有滑坡地质灾害的危险性和自身可能遭受已存在滑坡地质灾害的危险性。据此评估结果,为工程在建设过程中提出了具体的防治处理措施建议。     

电力工程中的地质灾害危险性评估

我国幅员辽阔,地质环境条件多样且复杂,导致了地质灾害发育强烈,种类繁多、危害较大。地质灾害与工程建设活动密切相关,既相互作用,相互依存,又相互影响[1]。电力工程项目遍布全国,特别是输电线路大多为人烟稀少的山区分布,线路长,跨越的地质地貌区域复杂,存在的地质灾害多种多样,或多或少面临着各种地质灾害的严重威胁[2]。通过电力工程地质灾害危险性评估工作的现状及评估工作流程的介绍,进一步分析电力工程地质灾害的危险性评估工作中存在的问题并提出了相应的看法。     

一种雷达和摄像机的空间标定方法

多传感器融合技术已经广泛应用在智能汽车环境感知领域中;雷达和摄像机的空间标定是伴随信息实时融合的道路检测技术的基础;结合智能汽车的实际应用,提出了针对激光雷达和摄像机的空间标定方法;通过特制的标定板来获得雷达数据和图像数据,选取激光雷达坐标系作为世界坐标系,通过参数拟合的方法来求取图像坐标系与雷达坐标系的变换关系,进而实现两种传感器的空间配准;该方法只需要标定板就能够完成激光雷达和摄像机的空间标定,标定精度高,实现了多个传感器世界坐标系的统一,避免了后续处理中数据解释的二义性;实验结果表明这种方法简单准确,满足系统要求。     

基于FPGA的雷达状态参数采集系统设计与实现

为使雷达回波模拟系统能够逼真地模拟真实作战环境,有效地对雷达各分系统进行测试,提出了雷达状态参数采集电路的设计方案,并给出了其软硬件实现;该方案以FPGA为核心,解决了不同状态参数的实时性采集问题,并通过制定串行传输协议,避免了数据传输过程中的冲突,保证了各状态参数传输的准确性,输出接口采用LVDS标准接口,确保了各状态参数的高速长距离传输;实验结果表明,该系统工作稳定,能够准确、实时地采集并传输雷达状态参数,且便于与其他数字电路的集成,满足系统设计的要求。     

Edge diffraction correction for 3D scattering center modeling based on the shooting and bouncing ray technique

In the traditional scattering center extraction method based on the shooting and bouncing ray(SBR) technique,only the physical optics contribution of the target is considered.However,the physical optics method is unable to describe the contribution of the actual edge diffraction.By comprehensively considering the specular reflection and edge diffraction contribution of the target,an edge diffraction correction method for 3D scattering center modeling based on the SBR technique is proposed.Using the SBR technique and combining the image domain ray tube integration and equivalent edge currents method,the edge diffraction correcting formula for the 3D inverse synthetic aperture radar image is derived.Simulation results show that the proposed method is able to effectively improve the modeling accuracy of the 3D scattering center.     

Method for discrimination of false targets in multistation radar systems based on the deep neural network

For the existing jamming discrimination methods for multistation radar systems,only the single feature of target echo space correlation is utilized as the metric,which leads to insufficient comprehensiveness of feature extraction,so that effectiveness and universality are insufficient for the discrimination algorithm.In this paper,an identification method in multistatic radar systems based on the deep neural network is proposed.This method combines the characteristics of multistatic radar systems cooperative detection technology,which has many available resources and strong scheduling ability in space,time and frequency domain,with the strong model learning and feature representation ability in the process of information processing on the deep neural network,so that it can effectively apply to the field of anti-deception jamming.Full use is made of unknown information about echo data to obtain more multi-dimensional,more comprehensive,more complete and deeper feature differences besides correlation,so as to achieve a better jamming discrimination effect.Simulation results show that the proposed method can effectively reduce the influence of noise and pulse number on the jamming discrimination performance.At the same time,the limitation of the target echo correlation coefficient on anti-jamming technology under nonideal conditions is alleviated,which broadens the boundary conditions of the application process.     

A Brief Introduction on Joint Radar and Communication Systems

Joint radar and communication(JRC) technology is gradually becoming an essential approach to alleviating spectral congestion. Radar and communications systems were designed with common spectral and hardware resources to reduce size, improve performance, reduce cost, and decongest the spectrum. Various approaches have been proposed to achieve the coexistence of radar and communication systems. This paper mainly focuses on the research directions of radar communication coexistence(RCC) and dual-function radar communication systems(DFRC) in JRC technology. We summarize and analyze the existing research problems in the JRC era. According to the characteristics and advantages of JRC technology, we highlight several potentials in military and commercial applications.     

基于多学习单元卷积神经网络的雷达辐射源信号识别

现有基于人工提取特征的复杂体制雷达辐射源信号识别方法时效性低,识别准确率不佳. 为此,提出了一种基于多学习单元卷积神经网络的识别方法. 首先对辐射源信号的模糊函数进行高斯平滑,以校正噪声带来的毛刺与畸变;然后提取其正交切片作为进一步的特征提取对象;最后构建多学习单元卷积神经网络,学习和提取正交切片深层、泛在的特征,并通过softmax分类器进行分类识别. 仿真实验结果表明,所提方法在信噪比为-2 dB时对6类典型雷达信号的整体平均识别率均保持在99.86%以上,即便是在-6 dB环境中,雷达信号的识别率也可达到88.50%,在极低信噪比条件下具有良好的性能和可行性.     

水工混凝土构件裂缝检测方法及应用

水利工程在长期运行过程中,其混凝土构件会出现磨蚀、裂缝等损伤,如这类问题不能够及时得到处理,将会严重影响水利工程的安全运行。本次研究主要针对水工混凝土构件中的裂缝检测方法进行研究,提出从“定性”到“定量”的一体化检测思路,具体实施过程分为四部分,即“普探—详查—判定—验证”,其中,普探阶段采用探地雷达法,确定裂缝的总体分布;详查阶段采用超声横波反射三维成像的方法,确定裂缝的纵深向分布;判定阶段分别采用裂缝测宽法和表面声波法,用来确定裂缝的表面宽度和贯穿深度;验证采用钻孔电视,确定探测结果的正确性和准确性。上述技术路线突破了单一方法的限制,可有效地运用于水工混凝土构件裂缝检测,经实践证实,具有良好的检测效果。     

快速三维扫描激光雷达的设计及其系统标定

为了增强传统二维激光雷达的环境几何信息采集能力,设计了一个快速三维扫描激光雷达系统.采用了一个单片机控制的步进电机旋转二维激光雷达的扫描平面,得到第三维扫描.研制了高速数据通讯卡,实现激光雷达与数据采集计算机之间的实时传输.在整个扫描过程中,各个时刻的二维距离测量数据和相应的俯仰扫描角被同时记录下来,形成一帧完整的三维距离图像.在分析系统误差源基础上,对初始姿态误差和系统偏心误差建立了数学模型,并采用最小二乘法作了系统标定.实验结果表明,该雷达系统精度高,在量程范围内距离误差小于±30 mm,扫描速度可达1.2 s/帧数据.