滤波对混沌信号分形维的影响

分形维数作为混沌系统的特征量,是分析混沌动力系统的重要工具,也是检测和识别混沌信号的依据.由于实际得到的混沌信号都存在着不同程度的滤波,忽略滤波影响会影响到分形维数的准确性.因此,通过理论推导,借助Lyapunov指数谱和Kaplan-Yorke维数,全面分析了从FIR滤波到一般的线性时不变滤波器对混沌信号分形维数产生...     

改进相空间重构方法在混沌识别中的应用研究

讨论了传统非线性时间序列相空间重构方法的特点,提出了一种改进的相空间重构方法.为了揭示非线性时间序列中的非线性相关性,采用了一种基于关联积分的统计量,并研究了不同参数对它的影响.研究了延迟时间和嵌入维数之间的关系,并采用时间窗口描述这2个参数的变化规律.同时,应用改进方法计算了混沌时间序列的重构参数,重构了混沌信号的吸引子.研究结果表明,该方法能够从时间序列有效地重构原系统的相空间,为混沌信号识别提供了新的途径.     

基于混沌理论的舰船辐射噪声特征提取

针对不同目标舰船的辐射噪声信号特征提取问题,提出了将混沌理论用于非线性时间序列的分析方法。该方法利用非线性局部投影滤波方法进行信号降噪,并在重构相空间的基础上对每一类舰船辐射噪声信号的最大Lyapunov指数、自然测度和关联维数等非线性特征参数进行提取。实验结果表明:当舰船辐射噪声信号的最大Lyapunov指数大于0且为有限值时,舰船辐射噪声信号具有混沌特性;自然测度和关联维数可作为区分不同目标船型的舰船辐射噪声信号的有效特征。     

基于尺度UKF小波网络的混沌时间序列预测

根据小波网络和Kalman滤波的特点,将小波网络的权重、平移因子和伸缩因子作为Kalman滤波的状态变量进行估计,提出一种训练速度快、泛化能力强的小波网络的尺度无轨迹Kalman滤波学习算法。然后,利用Rǒssler时间序列和变参数的Ikeda时间序列对该算法进行了验证。仿真结果表明,该算法预测混沌时间序列收敛速度快,预测精度高,而且在混沌时间序列的嵌入维数未知时也能取得较好的预测效果。可以胜任混沌时间序列的精确建模和预测任务。     

基于混沌高斯频率调制的距离-多普勒雷达成像

提出了利用混沌映射产生随机抽样的方法。进一步证实了高斯抽样可以通过两类随机数产生器结合混沌映射生成。利用高斯抽样,得到了高斯调频雷达信号。研究结果表明,通过这种高斯调频信号得到的模糊函数接近2维delta函数,它在距离-多普勒平面上的旁瓣是均匀分布的。对该高斯调频信号进行傅立叶处理可以得到高分辨率的距离-多普勒图像,图像品质较高。     

基于全维PI观测器的混沌系统鲁棒故障检测设计

研究了在干扰存在情况下基于全维PI观测器的混沌系统鲁棒故障检测设计问题。基于一类Sylvester矩阵方程的参数化解,给出了干扰和残差信号解耦的充要条件,并建立了具有鲁棒故障检测功能的全维PI观测器设计的参数化方法。Lorenz混沌系统的数值算例及其计算结果表明：在干扰存在的情况下,基于全维PI观测器的混沌系统鲁棒故障检测设计方法简单有效。     

航空发动机混沌支持向量预测模型应用

提出了一种基于混沌理论的通用支持向量预测方法.该方法通过重构相空间的饱和嵌入维数确定支持向量机的最佳输入变量的选取;通过计算混沌序列的最大Lyapunov指数确定支持向量机预测模型的最大有效预测步数;利用支持向量机强大非线性映射能力、网络结构的自动最优化特性,实现时间序列的非线性预测.最后,应用于压气机的试车数据序列建...     

航空发动机的可预测模型建模与应用

提出了一种基于混沌理论和支持向量的预测方法.通过重构相空间的饱和嵌入维数,确定支持向量机的最佳输入变量;通过计算混沌序列的最大Lyapunov指数,确定支持向量机预测模型的最大有效预测步数;利用支持向量机强大非线性映射能力、网络结构的自动最优化特性,实现时间序列的非线性预测.最后,应用于某型发动机压气机的试车时间序列数据建模与分析,结果证明该方法具有较高的预测精度.     

一种新的混沌雷达信号源的设计

基于考必兹(Colpitts)混沌振荡器,给出了一种新的混沌雷达信号源。这种混沌电路通过电容耦合两级改进型考必兹混沌电路实现。给出了基本改进型和两级耦合后的信号产生电路仿真结果,并对这种混沌雷达信号源和单级考必兹混沌电路的输出信号频谱进行了比较。     

混沌信号脉冲同步技术在雷达中的应用

依据混沌系统的脉冲同步特性,结合数字信号处理技术,可以得到原发射信号的时间延迟信号与变时间尺度信号.据此提出脉冲同步技术在混沌雷达系统中的应用方案,给出连续混沌信号雷达的结构框架.仿真实例以Colpitts电路产生的混沌信号为雷达发射信号,验证了基于脉冲同步的混沌信号时延与变时间尺度技术应用于混沌雷达的可行性.混沌雷达系统中的信号处理部分以宽带互模糊函数为工具,根据宽带模糊函数的定义给出了混沌信号雷达信号处理部分的详细框图.     

A novel signal feature extraction technology based on empirical wavelet transform and reverse dispersion entropy

Feature extraction is an important part of signal processing, which is significant for signal detection, classification, and recognition. The nonlinear dynamic analysis method can extract the nonlinear characteristics of signals and is widely used in different fields. Reverse dispersion entropy (RDE) proposed by us recently, as a nonlinear dynamic analysis method, has the advantages of fast computing speed and strong anti-noise ability, which is more suitable for measuring the complexity of signal than traditional permutation entropy (PE) and dispersion entropy (DE). Empirical wavelet transform (EWT), based on the theory of wavelet analysis, can decompose a complex non-stationary signal into a number of empirical wavelet functions (EWFs) with compact support set spectrum, which has better decomposition performance than empirical mode decomposition (EMD) and its improved algorithms. Considering the advantages of RDE and EWT, on the one hand, we introduce EWT into the field of underwater acoustic signal processing and fault diagnosis to improve the signal decomposition accuracy; on the other hand, we use RDE as the features of EWFs to improve the signal separability and stability. Finally, we propose a novel signal feature extraction technology based on EWT and RDE in this paper. Experimental results show that the proposed feature extraction technology can effectively extract the complexity features of actual signals. Moreover, it also has higher distinguishing ability for different types of signals than five latest feature extraction technologies.     

星上高功率放大器对导航信号载波跟踪性能的影响分析

由于滤波器带宽的限制和高功率放大器(HPA)的非线性效应,使恒包络导航信号产生失真,这将引起导航信号载波跟踪时锁相环(PLL)跟踪性能的恶化。针对这个问题,建立了星上高功率放大器非线性失真的一般模型,并推导了HPA的非线性效应引起PLL跟踪抖动的表达式,然后仿真分析了不同信号体制、不同滤波器带宽和不同功率放大器模型下的PLL跟踪抖动性能。     

非线性系统 Hammerstein 模型辨识

本文利用差集理论设计的输入信号。辨识了Hammerstein模型的脉冲响应函数,并进一步得到了非线性部分多项式的系数。本文得到的公式简单,辨识结果依概率收敛于真值。文中给出了仿真结果。     

多频激励微波非线性电路分析及其同伦连续算法

本文分析了微波非线性电路在多频激励下的频域稳态响应,给出了同伦连续这一简单有效的算法,结合实际计算了实例。     

Electronic warfare in the optical band: Main features,examples and selected measurement data

The paper presents the possibilities of, and methods for, acquiring, analysing and processing optical signals in order to recognise, identify and counteract threats on the contemporary battleground. The main ways electronic warfare is waged in the optical band of the electromagnetic wave spectrum have been formulated, including the acquisition of optical emitter signatures, as well as ultraviolet (UV) and thermal (IR) signatures. The physical parameters and values describing the emission of laser radiation are discussed, including their importance in terms of creating optical signatures. Moreover, it has been shown that in the transformation of optical signals into signatures, only their spectral and temporal parameters can be applied. This was confirmed in experimental part of the paper, which includes our own measurements of spectral and temporal emission characteristics for three types of binocular laser rangefinders. It has been further shown that through simple registration and quick analysis involving comparison of emission time parameters in the case of UV signatures in “solar-blind” band, various events can be identified quickly and faultlessly. The same is true for IR signatures, where the amplitudes of the recorded signal for several wavelengths are compared. This was confirmed experimentally for UV signatures by registering and then analyzing signals from several events during military exercises at a training ground, namely Rocket Propelled Grenade (RPG) launches and explosions after hitting targets, trinitrotoluene (TNT) explosions, firing armour-piercing, fin-stabilised, discarding sabots (APFSDS) or high explosive (HE) projectiles. The final section describes a proposed model database of emitters, created as a result of analysing and transforming the recorded signals into optical signatures.     

改进的GPS弱信号差分捕获方法研究

捕获是GPS接收机信号处理中的关键部分,在微弱信号情况下,传统的弱信号捕获方法不能很好地捕获到卫星信号,采用一种改进的GPS弱信号差分捕获方法来进行捕获研究.并对一组GPS的数据信息,分别采用以上2种方法对其进行捕获仿真.结果显示,新方法可以捕获到较微弱的GPS信号,提高了接收机的灵敏度.     

基于多态的大功率可控硅开关功率放大器的仿真研究

为了满足远距离海底探测对发射信号大功率的需求,选用了一种大功率可控硅开关功率放大器。在临界换流状态、自然换流状态和强迫换流状态三种工作状态下,分析了该功率放大器的工作原理,并建立了等效电路模型。运用OrCAD仿真了临界换流状态时的等效电路模型;在自然换流状态和强迫换流状态下,为获得功放电流值和负载电流值,对其等效电路模型分别建立了非线性数学模型,并通过MATLAB和VC 采用四阶Runge-Kutta法对非线性数学模型进行求解。以上方法能够求得该功率放大器电路在任意时刻、任意点处的电流电压值,能较客观、真实地反映电路的工作状况,对工程应用具有一定的参考价值。     

机载预警雷达对海上悬停直升机的探测

提出了依托机载预警(AEW)雷达利用悬停直升机旋翼回波信号来探测海上悬停直升机目标的方案．着重就机载预警雷达采用时－空级联自适应处理技术对海杂波的抑制和旋翼回波信号处理的方法进行了研究．最后,给出了计算机仿真结果．     

非晶丝GMI磁传感器信号的小波分析

当非晶丝巨磁阻抗效应( Giant Magneto-Impedance,GMI)磁探头输出数据的信噪比小于0时,常规的峰值检波方法难以检出真实信号.为此,提出了一种新的微弱信号检测方法,将非晶丝GMI磁探头的输出信号经放大、滤波和采样之后直接送入数字信号处理系统,利用小波变换方法提取微弱信号的特征,并利用相关分析法确定...     

A novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise,minimum mean square variance criterion and least mean square adaptive filter

Underwater acoustic signal processing is one of the research hotspots in underwater acoustics. Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing. Owing to the complexity of marine environment and the particularity of underwater acoustic channel, noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing. In order to solve the dilemma, we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), minimum mean square variance criterion (MMSVC) and least mean square adaptive filter (LMSAF). This noise reduction technique, named CEEMDAN-MMSVC-LMSAF, has three main advantages: (i) as an improved algorithm of empirical mode decomposition (EMD) and ensemble EMD (EEMD), CEEMDAN can better suppress mode mixing, and can avoid selecting the number of decomposition in variational mode decomposition (VMD); (ii) MMSVC can identify noisy intrinsic mode function (IMF), and can avoid selecting thresholds of different permutation entropies; (iii) for noise reduction of noisy IMFs, LMSAF overcomes the selection of decomposition number and basis function for wavelet noise reduction. Firstly, CEEMDAN decomposes the original signal into IMFs, which can be divided into noisy IMFs and real IMFs. Then, MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs. Finally, both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained. Compared with other noise reduction techniques, the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals, which has the better noise reduction effect and has practical application value. CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection, feature extraction, classification and recognition of underwater acoustic signals.