RDWT和NVF的自适应彩色图像水印算法

提出一种基于冗余小波(RDWT:Redundant Wavelet Transform)和噪声可见度函数(NVF:Noise Visibility Function)的彩色图像水印嵌入新算法,该算法利用主体图像的稳态广义Gaussian模型描述图像的局部特性,建立了水印信息的加权系数计算公式。在水印嵌入过程中先对主图像在YIQ空间的Y分量进行RDWT变换。同时利用NVF函数对经过加密变换的水印进行加权处理,然后利用奇异值分解将水印嵌入到变换域复杂纹理和边缘区域。实验结果表明该算法在保证水印不可见性的同时能很好地抵抗多种几何攻击和图像处理攻击,实现了水印鲁棒性、不可见性和容量的统一。     

CDF9／7小波在JPEG2000中的整数变化

通过添加系数缩放值叮,使目前在JPEG2000中广泛采用的CDF9／7小波提升算法完全整数化,不仅保持了现有算法的还原度,而且简化了算法的复杂度,在图像压缩上取得了比较满意的效果。     

基于MHMM-SVM混合模型的雷达发射机性能退化状态监测

雷达发射机结构复杂,早期性能退化状态呈现多样化。为此,给出了一种MHMM-SVM故障诊断模型。该模型首先通过小波分析来提取观测信号的特征向量,然后从提取的特征向量中提取部分作为样本完成模型训练并生成发射机各种退化状态的分类器,最后给出诊断结果。仿真验证结果表明:MHMM-SVM故障诊断模型对电子装备早期故障的识别率明显优于MHMM、SVM单一模型。     

基于双自适应小波变换的JPEG2000数字水印算法

针对小波变换提出一种双自适应算法,并将该算法应用于JPEG2000压缩过程中的小波变换,在此基础之上,又提出一种适合于JPEG2000的数字水印算法．该算法利用JPEG2000压缩中的双白适应小波变换,将水印嵌入到量化后的小波系数中．实验证明,该算法具有较好的不可见性和较强的鲁棒性．     

高灵敏度GPS软件接收机捕获算法

针对传统方法难以实现室内环境下GPS信号的捕获以及传统弱信号捕获算法的一系列弊端,介绍了一种新的捕获算法。由于数据段通过“先累加后相关”的方式进行相干积分可以减少运算量,以此方法改进的半位捕获法进行数据段选择可以避免导航数据位翻转,并且改进后的差分相干积分能够减小非相干积分造成的“平方损失”和改善信噪比,融合上述优点,提出了一种新的算法,为高灵敏度GPS软件接收机的实现提供了保证。仿真结果表明,此算法极大的提高了接收机的捕获性能,并且能够快速捕获到载噪比低至25dB/Hz的微弱信号。     

基于分数傅立叶变换的图像隐藏技术

提出一种利用分数傅立叶变换实现图像隐藏的新方法。将多个不同图像的信息分别经不同阶的分数傅立叶变换后,记录在同一谱平面上,实现了图像的第一重隐藏。通过相位编码后,对谱平面进行另一次分数傅立叶变换,实现图像的第二重隐藏。在解密时,需要特定的分数阶及正确的解码相位才能再现不同的初始图像。提出的新方法用不同的分数阶实现多幅图频谱叠加的互不干扰,用相位编码使得两次的分数傅立叶变换互不干扰,随机设置多重密钥,且每重密钥都极其重要,使得破译的难度提高,因此图像加密的结果更加安全。     

基于整数小波变换的多光谱遥感图像压缩技术

在分析多光谱图像小波变换后系数特点的基础上,提出了一种基于整数小波变换的3维集合分裂嵌入块编码(3D SPECK)压缩方法。该方法将小波变换压缩技术中的零树编码推广到多光谱图像压缩中,采用整数小波变换去除空间冗余,对单波段图像,采用2D SPECK编码,对多波段图像,谱域上构成的小波矢量采用离散余弦变换(DCT)进行变换,对变换后的系数进行3D SPECK编码。实验结果表明,该方法硬件实现简单,编码解码时间快,对内存要求低。     

基于Hough变换的高分辨SAR图像道路目标检测

给出了一种检测高分辨率SAR图像道路目标的方法。该方法首先对原始图像进行一系列的预处理,然后利用Hough变换识别道路。在提取感兴趣区域时,该算法采用了高斯概率迭代方法代替了一般的CFAR检测或阈值分割;在进行Hough变换时,该算法用平均Hough变换代替一般的Hough变换;在检测变换域峰值点时,采用了结合全局CFAR检测的最大值点搜索的方法;最后对检测出来的准道路,该算法根据道路的独具性质进行鉴别,以确定其是否确实为道路。使用MSTARRedstone实测杂波图进行实验,取得了满意的效果。     

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.     

Cooperative interception with fast multiple model adaptive estimation

For the case that two pursuers intercept an evasive target, the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target, which performs a bang For the case that two pursuers intercept an evasive target, the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target, which performs a bang-bang evasive maneuver with a random switching time. Combined Fast multiple model adaptive estimation (Fast MMAE) algorithm, the cooperative guidance law takes detection configuration affecting the accuracy of interception into consideration. Introduced the detection error model related to the line-of-sight (LOS) separation angle of two interceptors, an optimal cooperative guidance law solving the optimization problem is designed to modulate the LOS separation angle to reduce the estimation error and improve the interception performance. Due to the uncertainty of the target bang-bang maneuver switching time and the effective fitting of its multi-modal motion, Fast MMAE is introduced to identify its maneuver switching time and estimate the acceleration of the target to track and intercept the target accurately. The designed cooperative optimal guidance law with Fast MMAE has better estimation ability and interception performance than the traditional guidance law and estimation method via Monte Carlo simulation.