最优仿生大棕蝙蝠声波信号模型及雷达测距

从蝙蝠终端捕食阶段发出的声波信号入手,提出了三种最优化仿生大棕蝙蝠声波信号构建模型,并有效应用于雷达测距并得以实现。模型构建以最接近蝙蝠终端捕食过程中发出的声波信号为原型,采用宽带信号处理方法在设计雷达仿生蝙蝠终端捕食波形,并对线性调频、指数调频和对数时间三种模型的雷达测距算法进行具体研究,采用宽带模糊度函数对三种声波信号进行仿真分析,最后成功构建了雷达测距系统中距离分辨性能最好的最佳仿生声波信号模型。仿真结果表明,二次谐波对数调频信号模型及对应的雷达测距算法的距离分辨性能最好,具有较高的雷达测距精度。研究结果为有效利用仿生信号模型于雷达测距具有重要的参考应用价值。     

基于平方法的雷达信号脉内调制粗识别

为了能够简单快速识别出雷达信号脉内调制类型,提出了一种简单高效的调制类型粗识别方法。首先对输入信号进行平方处理,经FFT变换得到其频谱并计算出平方后的信号3d B带宽,最后将平方后的信号3d B带宽与阈值比较,粗识别出调相信号和调频信号。仿真结果表明,该方法使输入信号的频谱特征更加明显,验证了该方法的有效性和实用性。     

基于MATLAB的两种数字调制方式仿真与分析

调制作为通信系统组成必不可缺的一部分,主要用于将基带信号进行一定的处理加到载波上,使其变为适合于信道传输的形式。传统的调制分为调幅、调频、调相3种方式,在数字通信系统中,采用QPSK调制和QAM调制较多,文章通过通信系统中常用的MATLAB仿真软件,选取QPSK调制和16QAM调制方式,通过对其星座图以及误码率进行对比仿真,得出两种调制方式的优缺点。     

基于高阶累积量和循环谱的调制方式识别研究

接收机采用高阶累积量和循环谱这两种低信噪比的算法,对检测到的各种信号进行调制方式识别,其中包括BPSK、QPSK、2FSK、4FSK、MSK等5种已调信号,然后,通过MATLAB编程,进行系统仿真,仿真结果表明,循环谱对高斯噪声有更强抑制作用。     

基于时频分析的动目标检测技术

利用逆合成孔径雷达成像技术,基于时频分析的性质,提出一种雷达目标识别的增强方法,对雷达回波信号采用距离多卜勒图技术,得到信号回波的距离多卜勒图,从中提取目标的各项参数,达到增强和检测的目的。经仿真实验证明,能够在强杂波背景下精确识别目标。     

G-K聚类分析在雷达信号分选中的应用

针对传统的聚类算法对雷达信号分选率不高的问题,尝试将G-K聚类分析应用于雷达信号分选中.仿真结果证明,相比于传统的聚类算法,该方法具有良好的雷达辐射源识别能力.     

雷达波模糊度函数控制飞控智能距离估计算法

提出一种改进的飞控雷达系统雷达波模糊度函数控制距离估计算法,并将算法有效进行移植进行软件设计,应用于飞机飞行控制智能距离估计系统中。采用对数调频信号的调频形式构建飞控距离估计信号模型,分析了该信号的模糊度函数及对距离的分辨力。提取攻击和地面目标检测性能曲线,设计了飞控智能距离估计软件系统并进行安装调试。算法仿真实验和软件系统测试性能表明,相比传统的线性调频波信号雷达距离估计,改进的飞控距离估计算法能在信噪比极低环境下,随着飞机姿态速度的快速变换,距离估计结果精确。软件设计平台具有较好友好性和人际交互性,能有效移植到飞机控制平台中。     

强干扰多载波雷达信号幅度检测算法

在强干扰环境下对多载波雷达信号的幅度检测是实现空中目标打击的关键技术。传统方法中,对雷达信号的幅度检测采用时频耦合算法,如果信号和背景噪声有很强干扰和多载波特征时,检测性能不好。提出一种基于分数阶Fourier时频耦合的信号幅度检测算法。构建强干扰环境,描述雷达信号的宽度和深度等特征量,对多载波雷达信号进行分数阶Fourier变换,对目标回波的尺度和时延进行估计,求解相位模糊数搜索结合解,得到雷达信号参数相位补偿结果,根据雷达信号特征量聚点塑造特征量模型运算雷达信号的特征量聚点,获取高特征量聚集区域,通过后置的高阶累积量切片,使信号的累积量增大,而噪声被抑制提高了检测精度。仿真实验表明,算法的检测概率较高,幅度检测能很好地跟踪信号幅度的变化。     

基于污点数据聚类的攻击调频信号虚警预估计

对网络攻击信号的线性调频建模和虚警门限预估计,是实现网络攻击信号检测的基础。传统方法采用路由数据交换的阵列信号聚类方法进行信号建模和虚警门限估计,由于攻击信号具有多波束自相似特征,导致虚警门限的预测精度不高。提出一种基于污点数据双模聚类的攻击调频信号虚警预估计算法,采用频率调制规律设计网络攻击信号模型,对攻击信号进行双线性核相位加权,得到脉冲压缩后的攻击信号二次调频信号输出。在双模聚类的时频平面内实现对信号检测能量的聚集,以改善信号分量聚集的尖峰,实现了对攻击调频信号的时间点与频率点的重组排列和特征分布估计,达到对虚警门限准确估计的目的。实验结果表明,该算法能有效估计攻击调频信号的虚警门限,对网络攻击信号的虚警门限的预估计精度达到98.5%,提高了对攻击信号的参数估计和信号检测能力,提高检测概率,优越性明显。     

敌我识别S模式信号调制特征分析

S模式的调制信号中包含了丰富的目标状态参数。介绍了S模式询问和应答信号导前脉冲和数据段的调制方式,分析了S模式敌我识别信号调制方式与功率谱密度、信号带宽的关系。仿真生成S模式询问和应答的基带信号,通过短时傅里叶变换分析信号的局部时频特性。敌我识别S模式信号调制特征的分析为对敌我识别信号的截获解译提供了理论支持。     

Micro-Doppler-based target detection and feature extraction in indoor and outdoor environments

In many cases, a target or a structure on a target may have micro-motions, such as vibrations or rotations. Micro-motions of structures on a target may introduce frequency modulation on the returned radar signal and generate sidebands on the Doppler frequency shift of the target's body. The modulation due to micro-motion is called the micro-Doppler (m-D) phenomenon. In this paper, we present an effective quadratic time-frequency S-method-based approach in conjunction with the Viterbi algorithm to extract m-D features. For target recognition applications, mainly those in military surveillance and reconnaissance operations, m-D features have to be extracted quickly so that they can be used for real-time target identification. The S-method is computationally simple, requiring only slight modifications to the existing Fourier transform-based algorithm. The effectiveness of the S-method in extracting m-D features is demonstrated through the application to indoor and outdoor experimental data sets such as rotating fan and human gait. The Viterbi algorithm for the instantaneous frequency estimation is used to enhance the weak human m-D features in relatively high noise environments. As such, this paper contributes additional experimental m-D data and analysis, which should help in developing a better picture of the human gait m-D research and its applications to indoor and outdoor imaging and automatic gait recognition systems.     

基于局域波分解的机动目标ISAR成像

在飞机一类惰性较大的目标作机动飞行时,在ISAR成像的短时间内,同一距离单元的回波可近似看成多分量线性调频信号的叠加,且调频斜率各不相同. 因此,在ISAR成像的横向压缩处理中,先利用局域波分解方法将每个距离单元的回波分解为若干个线性调频信号,然后再进行Wigner-Ville变换(WVT),获取目标的瞬时多普勒谱,进而实现目标的二维成像. 该处理方法不仅避免了WVT交叉干扰项的产生,而且在保持WVT的高时频分辨能力的同时减少了计算量,仿真结果证明了该方法的有效性.     

基于压缩采样的模拟到信息转换的研究与实现

传统通信设备在处理宽带频谱稀疏信号时,面临高采样率、快处理速度等方面的困难。提出了一种基于压缩采样（Compressive Sensing,CS）的模拟到信息转换方法,解码端采用一定的算法对信号精确重构。仿真结果表明,与传统方法相比较,采样率降低了,并呈现出很好的恢复性能。     

经验模式分解去斑和顶帽变换在舰船检测预处理中的应用

提出了一种基于经验模式分解去斑和顶帽变换背景不均匀的预处理方法. 经验模式分解去斑算法先对图像每一列进行经验模式分解得到IMF函数,然后将原信号与第一、二模态相减得到初步处理图像,再对该图像每一行重复该操作从而得到去斑图像,该算法有效地去除斑点噪声;顶帽变换则有效地补偿了海浪带来的局部不均匀的背景亮度,提高了图像的信杂比,有利于目标的检测. 仿真结果证明了算法的有效性.     

Lattices sampling and sampling rate conversion of multidimensional bandlimited signals in the linear canonical transform domain

The linear canonical transform (LCT) has been shown to be a powerful tool for optics and signal processing. Many theories for this transform are already known, but the uniform sampling theorem, as well as the sampling rate conversion theory about arbitrary lattices sampling in the LCT domain are still to be determined. Focusing on these issues, this paper carefully investigates arbitrary lattices sampling, the sampling with separable matrices and nonseparable matrices, to obtain uniform sampling theorem and the sampling rate conversion theory in the LCT domain. Firstly, the spectral expression of the discrete-time signal sampled via arbitrary lattice is deduced in the LCT domain. Based on it we propose the alias-free sampling relationship between two matrices and present the perfect reconstruction expressions for bandlimited signals in the LCT domain. Secondly, for further research on discrete signals to obtain sampling rate conversion theory, we define the multidimensional discrete time linear canonical transform (MDTLCT), as well as the convolution for the MDTLCT. Thirdly, the formulas of multidimensional interpolation and decimation via integer matrices in the LCT domain are derived. Then, based on the results of interpolation and decimation, we make analyses of the sampling rate conversion via rational matrices in the LCT domain, including spectral analyses and the formulas in time domain. Finally, simulation results and the potential applications of the theories are also presented.     

基于MUSIC算法的人体呼吸信号检测

基于MUSIC算法可以很好的区分多个目标信号的特点,用它对生命探测雷达接收的人体呼吸回波信号进行理论推导,并用MATLAB进行仿真,根据峰值频率的情况可以看出胸腔幅度是否发生变化,来证明生命体有无呼吸,即可证明有无生命体存在。     

Communication modulation signal recognition based on the deep multi-hop neural network

Automatic modulation classification (AMC) is one of the core technologies in non-cooperative communication. In the complex wireless environment, it is not easy to quickly and accurately recognize the modulation styles of signals by conventional methods. The deep learning method (DLM) can deal with the problem and achieve good effects. In conventional DLMs, the length of input data is fixed. However, the signal length in communication is changing, which may not make full use of the DLMs’ input signal information to improve the recognition accuracy. In this paper, the deep multi-hop convolutional neural network (CNN) is employed to learn the time-domain signal features with different lengths. The proposed network includes the multi-hop connection rate and the receptive field extension scope to dispose of the limitation. The experiment shows that the proposed network can achieve better recognition results under the sparse multi-hop network structure. The reception field extension scope is also conducive to further improve the recognition effects. Finally, the proposed network has shorter training time and smaller parameters, which is more convenient for training the network and deploying in the existing communication system.     

基于快速傅立叶变换的信号频谱分析仿真

信号序列的DFT本身就是信号频谱的采样集,所以DFT可直接用于分析信号的频谱。快速傅立叶变换是一种有效、实用的信号DFT算法。文章通过对DIT-FFT算法的详细分析,提出了一种计算机软件仿真信号频谱分析方案,并给出了关键算法的C语言程序。     

多基线SAR三维成像的QR分解算法

根据多基线合成孔径雷达(SAR)三维成像的信号模型,得到了利用高度向观测数据实现目标三维成像的矩阵方程,并引入QR分解算法求解矩阵方程,形成了多基线SAR三维成像的QR分解算法.使用该算法对多基线SAR仿真数据进行了三维成像实验.     

电力故障录波监测系统设计与研究

电力工业是关系到国计民生的支柱产业,在国民经济中具有非常重要的地位。因此,保证电力系统安全可靠地运行和电能的质量具有非常重要的意义。本文借助高性能嵌入式处理器及高速数字信号处理器(DSP),以《220～500kV电力系统故障动态记录技术准则》为基础设计了电力故障录波监测系统,采用测频算法对系统频率进行检测、谐波处理,并进行深入算法研究及仿真。