相位编码脉冲串的相参性识别算法研究

本文提出了一种相位编码脉冲串的相参性识别算法,通过检测脉冲相位的线性度判别序列的相参性。该算法通过M次方运算将相位编码信号变换成正弦波信号,再通过相参积累,有效地提高了信号的信噪比。文中讨论了算法的信噪比门限,并通过仿真实验加以验证。仿真实验表明,本方法可以在一定信噪比条件下实现对相位编码脉冲串相参性的识别。本文算法有助于实现对脉冲多普勒雷达的识别和告警,对研制数字射频存储器也具有重要意义。     

基于相参处理的宽脉冲信号参数估计快速算法

提出了一种宽脉冲信号参数估计快速算法,利用抽取的方法来降低宽脉冲信号参数估计的计算量。首先,对宽脉冲正弦波信号进行抽取构造相参脉冲串,利用相参脉冲频率估计算法进行频率估计。其次,对于宽脉冲线性调频信号,对其做延时相关之后即可转化为宽脉冲正弦波信号,然后在上述抽取和相参处理方法的基础上即可得到宽脉冲线性调频信号参数估计值。性能分析和仿真结果表明,该算法在保证一定的参数估计精度前提下,大大降低了算法的计算量,有利于宽脉冲信号参数估计的实时处理。     

相参脉冲信号识别方法和性能分析

提出了一种相参脉冲信号识别算法,通过检测脉冲相位的线性度判别序列的相参性.该算法通过相参积累,有效地提高了信号的信噪比.文中讨论了门限的选取对算法性能的影响,给出了相应的数学推导,并通过仿真实验对推导结果加以验证.同时还讨论了算法的信噪比门限.仿真实验表明,本方法可以在较低信噪比情况下实现对脉冲信号相参性的识别.本文算法有助于实现对脉冲多普勒雷达的识别和告警,对研制数字射频存储器也具有重要意义.     

调频脉冲串相参合成研究与FPGA硬件实现

介绍了利用调频脉冲串合成宽带信号的原理,进行了合成宽带信号的仿真。给出了基于FPGA的调频脉冲串产生的硬件实现电路。利用该电路得到了中频信号的测量波形,通过后处理将3个30MHz带宽的子脉冲合成了等效带宽90MHz的信号。验证了用调频脉冲串合成宽带信号来提高雷达距离分辨率方法的正确性和有效性,及其工程可行性。     

脉冲噪声环境下的韧性匹配滤波检测方法

针对传统局部最优匹配滤波检测性能在脉冲噪声环境下会明显退化的问题,提出一种韧性匹配滤波检测方法。利用噪声分布概率密度函数求出非线性变换公式,对观测信号作非线性变换和匹配滤波检测。应用于BPSK调制信号的匹配检测滤波信号仿真表明,该算法在抑制脉冲噪声方面对噪声特征指数具有良好韧性,在脉冲噪声环境下比传统局部最优匹配滤波方法具有更好的检测性能。     

重频参差相参脉冲串频率估计算法

研究了重频参差相参脉冲串的频率估计算法。通过脉内相关积累,提高了新序列的信噪比。对新序列的相位差按参差重数抽样平均,减小了等效的相位噪声方差,利用重频参差比解相位模糊,扩大了频偏允许范围,降低了算法的信噪比门限。分析了本算法实现相参频率估计的条件,推导了相应信噪比门限的解析表达式,指出了信噪比门限与信号样本总数、参差重数、参差比之间的关系。仿真结果表明:上述结论是正确的,在满足信噪比门限条件下,频率估计的精度接近相参脉冲串频率估计的克拉美-罗限(CRLB)。     

相参脉冲串多普勒频率变化率估计算法

给出了一种新的利用相参脉冲串进行多普勒频率变化率估计的算法,选取准最佳算法估计频率,减小了频率估计误差.对各脉冲进行脉内相关积累,将相参脉冲串变换成一个新的线性调频信号序列.该序列的样本点数是脉冲个数、采样间隔是脉冲重复周期、调频斜率是多普勒频率变化率、信噪比提高为输入信噪比的N倍(N是脉内采样点数).因此,可以在较低的信噪比条件下精确估计多普勒频率变化率.仿真结果表明,该算法能在比现有算法更低的信噪比条件下精确估计多普勒频率变化率,其性能接近相参脉冲串多普勒频率变化率估计的克拉美-罗限.     

串脉冲超声导波断轨检测技术研究

针对传统的钢轨断轨检测效率低、无法进行实时检测的不足,对串脉冲超声导波断轨检测方法进行了研究.首先为了增强发射能量,同时达到脉冲叠加的目的,研发了脉冲个数、脉宽、间隔均可调的高压串脉冲超声导波发射系统;为了保证接收信号的信噪比和空间精度,用匹配滤波的方法对接收信号进行脉冲压缩.采用研制的串脉冲超声导波断轨检测系统,选用SH导波换能器在钢轨上进行了检测实验.结果表明,检测系统性能稳定,经过串脉冲叠加,使接收信号的幅值约为单脉冲接收信号的3倍,经过匹配滤波后,信噪比得到明显提升.该方法的换能器置于轨腰,能够对钢轨实现在线监测.     

基于有限状态机的角度信息采集电路设计

利用编码器的光电信号转换功能,能够将天线角度信息转换为脉冲信号;编码器输出A、B、Z三相脉冲信号,用FPGA对三相信号进行接收,实现角度信息的采集;根据A、B相脉冲的特点,通过检测A、B相信号的电平变化,采用有限状态机的方法,产生辨向信号和计数脉冲信号,这种方法克服了由于脉冲干扰和天线抖动带来的影响;通过设计可逆计数器,实现对脉冲的积累计数;同时设计能够与串口进行通信的并行—串行数据转换电路,便于进行数据处理;设计锁存器,保证数据转换不发生错误;仿真结果证明电路有较强的抗干扰性。     

超宽带无线电系统跳时技术的研究

超宽带无线电系统中最常用和最典型的信号是时域上的极窄脉冲串。为了使超宽带系统的功率谱进一步平滑,增强系统隐蔽性和保密性,超宽带无线电系统往往都要破坏窄脉冲串的周期性,使所发射的相邻窄脉冲间的时间间隔进行伪随机的跳变。文章对超宽带基本理论、窄脉冲串时间间隔跳变的相关原理进行了介绍,提出了具体实现方法,给出了对实验样机的测试结果。     

PLC在车轴超声自动探伤系统中的应用

对车轴超声自动探伤作业的流程控制进行了研究,提出了工控机与PLC相结合的设计方案;采用“指令”与“报告”形式的应用层协议便捷的实现了二者问通讯;通过PLC的高速脉冲串功能（PTO）驱动步进电机实现了探头精确定位;基于模块化思想的软硬件设计,使得系统更易于调试和维护。     

Range,radial velocity,and acceleration MLE using frequency modulation coded LFM pulse train

Linearly frequency modulation (LFM) pulse train and linearly stepped frequency (LSF) pulse train are mostly used in radar systems. However, the estimation performance of target motion parameters may be affected by the high recurrent lobe levels and the range–Doppler coupling phenomenon appearing in ambiguity function (AF). In multi-target scenarios, the estimation performance becomes even worse. The Costas frequency-modulation coded (CFMC) LFM pulse train has an ideal thumbtack-shaped AF, thus it can provide motion parameter estimation with high precision. However, the estimation of target motion parameter for the coherent CFMC LFM pulse train has not been investigated in depth. In this paper, we first analyze the properties of the AF of the CFMC LFM pulse train. Based on its convexity and narrow mainlobe width, a fast method to implement the maximum likelihood estimator (MLE) is proposed to estimate the motion parameters. To reduce the computation complexity, the Chirp-Z Transform (CZT) is introduced. Then, the Cramer–Rao lower bounds (CRLBs) on range, velocity and acceleration for frequency-modulation coded (FMC) pulse train are derived. It is shown that the CRLBs are relevant to the frequency coding patterns. Finally, Monte Carlo simulations are performed to verify the performance of the MLE. The results show that the performance of our proposed method can achieve the CRLBs when the signal-noise ratio (SNR) is higher than the threshold SNR.     

基于高次多项式动力凸轮型线的配气机构性能分析

为分析高次多项式动力凸轮型线的特性参数对配气机构性能的影响,以某高速汽油机双顶置凸轮轴配气系统为研究对象,基于配气机构运动学和动力学计算理论,建立高次多项式动力凸轮型线的数学模型.在AVL-TYCON中建立运动学和动力学仿真模型,计算并分析丰满因数、正加速脉冲宽度和润滑因数等关键评价指标的变化规律,结果表明：随着各阶幂指数的增大,丰满因数逐渐增加,发动机获得较好的充气性能,但正加速度宽度逐渐减小,配气机构工作平稳性变差.     

磁悬浮列车脉宽编码式绝对定位系统

介绍了一种脉宽编码感应式绝对定位系统在磁悬浮列车中的应用。主要讨论了系统的构成和定位原理。在以上分析的基础上,结合我国磁悬浮列车的发展情况,提出了适合现阶段列车定位的方案,并介绍了相关试验结果。     

2kV高稳定度高压脉冲电源设计

在一些光学精密仪器的应用场合中,不仅需要脉冲电源在时间上能够提供精确的控制,而且需要具有高稳定度的输出,以提高光电系统的探测性能;运用基于高压开关的两级式方法,采用单级高功率MOSFET开关结合具有高稳定输出的直流电源的结构,设计了输出辐度可达2kV的高稳定负脉冲电源;测试结果表明,在输出脉冲宽度为8 μs时,脉冲前沿约为48 ns,系统延迟时间约为140 ns,负脉冲超调参数约为1%。该系统具有结构简单、可靠性高、高稳定性输出等优点,可以为特定的光电器件提供优质的控制方式。     

基于数据抽取的相参脉冲串载频精确估计方法

利用相参脉冲串信号的相位相干特性,提出一种利用FFT和数据抽取技术的精确测量载频的新方法。该方法利用数毫秒信号,达到了亚kHz量级的频率估计精度和数十Hz量级的频率分辨能力。仿真试验验证了该方法的频率精确测量能力和频率高分辨能力。     

Color pornographic image detection based on color-saliency preserved mixture deformable part model

To utilize the rich semantic information of sexual organs, we propose a new framework for pornographic image detection based on sexual organ detectors. Traditional sexual organ detectors are built on shape features. Since the color distribution of sexual organ in same pose is consistent, color is an important visual clue to represent sexual organs. We use color attribute to describe the local color of sexual organs and concatenate it with histogram of oriented gradients based shape feature to represent sexual organs. Based on the concatenated feature, we train sexual organ detectors by the color-saliency preserved mixture deformable part model (CPMDPM). We detect pornographic images sequentially with sexual organ detectors. In experiments, the optimal part number of the deformable part model is chosen experimentally. We evaluate the performance of each CPMDPM based sexual organ detector, which is superior over the shape feature based detector. The proposed pornographic detection method is superior over methods based on low level features of skin regions, bag of words model and color incorporated SIFT features etc.     

Treetop detection using convolutional neural networks trained through automatically generated pseudo labels

ABSTRACT

Using remote sensing techniques to detect trees at the individual level is crucial for forest management while finding the treetop is an initial and important first step. However, due to the large variations of tree size and shape, traditional unsupervised treetop detectors need to be carefully designed with heuristic knowledge making an efficient and versatile treetop detection still challenging. Currently, the deep convolutional neural networks (CNNs) have shown powerful capabilities to classify and segment images, but the required volume of labelled data for the training impedes their applications. Considering the strengths and limitations of the unsupervised and deep learning methods, we propose a framework using the automatically generated pseudo labels from unsupervised treetop detectors to train the CNNs, which saves the manual labelling efforts. In this study, we use multi-view satellite imagery derived digital surface model (DSM) and multispectral orthophoto as research data and train the fully convolutional networks (FCN) with pseudo labels separately generated from two unsupervised treetop detectors: top-hat by reconstruction (THR) operation and local maxima filter with a fixed window (FFW). The experiments show the FCN detectors trained by pseudo labels, have much better detection accuracies than the unsupervised detectors (6.5% for THR and 11.1% for FFW), especially in the densely forested area (more than 20% of improvement). In addition, our comparative experiments when using manually labelled samples show the proposed treetop detection framework has the potential to significantly reduce the need for training samples while keep a comparable performance.     

一种基于多变量Logistic模型的缺陷定位方法

缺陷定位是软件开发过程的重要环节。充分利用程序的结构特征和行为特征有助于提高缺陷定位效率。提出一种基于多变量Logistic回归分析的缺陷定位框架, 用于软件演化时对新版本程序进行类方法级别的缺陷定位。首先设计一组度量结构特征和行为特征的指标, 通过静态分析和测试程序搜集并构建旧版本程序的特征数据集, 同时从缺陷跟踪系统获取旧版本缺陷信息;其次, 基于所得特征数据集和缺陷信息, 应用单变量分析筛选出度量指标中与缺陷显著相关的指标, 随后用选中的显著指标展开多变量分析, 训练多变量Logistic模型;最后, 基于选出的显著指标搜集并构建新版本程序的特征数据集, 运用得到的Logistic模型预测每个类方法的出错概率, 进而按出错概率降序检查类方法以定位错误。基于一组开源程序进行缺陷定位实证研究,结果表明, 多变量Logistic模型可以提高缺陷定位的效率。关键词：