乘性色噪声和加性双值噪声作用下延迟双稳系统中的随机共振

研究了在乘性和加性色噪声以及加性非对称双值噪声作用下方波信号驱动的延迟双稳系统中的随机共振现象。基于小延迟近似,在绝热近似条件下推导出了系统输出信噪比(Signal-to-Noise Ratio, SNR)的解析形式。分析结果表明,随着双值噪声强度与非对称性参数、方波信号幅度以及乘性和加性色噪声强度的变化,SNR表现出了随机共振行为。随着延迟时间、色噪声相关时间和系统参数的增大,SNR作非单调变化。     

关联噪声和周期信号驱动的非对称双稳系统的稳态分析

运用Liouville equation和诺维科夫原理,解出了关联噪声和周期信号共同驱动的非对称双稳系统的近似福克-普朗克方程,并求解了其稳态概率密度函数.在此基础上,分析了乘性噪声强度 、加性噪声强度 、噪声间关联系数 ,周期信号振幅 、频率 以及系统非对称参数 等对稳态概率密度分布曲线的影响.结果表明:(1)噪声强度及其关联、周期信号振幅、系统非对称参数的改变均能引起稳态概率密度分布曲线单峰结构和双峰结构之间的转换,即能够诱导非平衡相变产生;(2)周期信号频率改变时,没有非平衡相变发生;(3)当系统非对称参数为零时,稳态概率密度分布曲线具有关于 的对称结构;当系统非对称参数不等于零时,其对称结构被破坏.     

Transition of a noncavity bistable exciton system driven by correlated external noise to a strongly absorbing state

The correlated action of additive and multiplicative noise interferences on an exciton bistable system results in the disappearance of the weakly absorbing state, with the system’s behavior turning from reversible to irreversible. The strongly absorbing state of the bistable system shifts to the region of higher exciton concentrations.     

The method of separating harmonic signals from multiplicative and additive noises

This paper focuses on the extraction of a harmonic signal from multiplicative and additive noises. A method is proposed in two stages: (1) to square the original discrete time series, which in- cludes both signals and noises, and form a new time series. By this means, the multiplicative noise is converted to additive noise; and (2) to filter out the noise by using existing noise removal schemes. With a large amount of simulation, experimental results demonstrated the efficiency and effective- ness of this newly developed method in terms of Signal-to-Noise Ratio (SNR) and other criteria. From the experiment, it is also found that: the two kinds of noises affect the SNR differently. In gen- eral, the SNR is not influenced by multiplicative Gaussian noise regardless of its variance. However, if both kinds of noise exist, the SNR decreases with the incensement of the Variance of Additive Noise to Multiplicative Noise Ratio (VAMNR). This analysis is also supported by simulation work.     

基于广义协方差矩阵的乘性和加性噪声中的谐波恢复

针对非零均值乘性噪声中的谐波恢复问题,本文提出一种基于广义协方差矩阵的乘性噪声中谐波个数和频率的估计方法。首先定义一类广义协方差并构造广义协方差矩阵,通过对广义协方差矩阵进行特征值理论分析,得到了非零均值乘性噪声中谐波分量个数与协方差矩阵特征值之间的内在联系,这个性质可以用来估计谐波分量个数。而且利用子空间旋转不变性技术,可以从协方差矩阵中估计出谐波的频率。本文所提方法对于乘性和加性噪声的颜色和分布均无任何假设,可以应用于任意分布和任意颜色的乘性和加性噪声中的谐波恢复。仿真实验表明,本文所提谐波恢复方法具有很高的频率分辨率。      

A convex variational method for super resolution of SAR image with speckle noise

Super resolution (SR) is an attractive issue in image processing. In the synthetic aperture radar (SAR) image, speckle noise is a crucial problem that is multiplicative. Therefore, numerous custom SR methods considering additive Gaussian noise cannot respond to this image degradation model. The main contribution of this paper is to propose a novel variational convex optimization model for the single SAR image SR reconstruction with speckle noise that is one of the first works in this field. Employing maximum a posteriori (MAP) estimator and proposing an effective regularization based on combination of sparse representation, total variation (TV) and a novel feature space based soft projection tool to use merits of them is the main idea. To solve the proposed model, the split Bregman algorithm is employed efficiently. Experimental results for the multiple synthetic and realistic SAR images show the effectiveness of proposed method in terms of both fidelity and visual perception.     

乘性噪声环境下基于局部最佳检测器的伪码捕获方法

扩频系统常常工作在多径环境中,伴随着加性噪声的同时往往还存在着乘性噪声。该文提出了一种乘性噪声环境下伪码捕获方法,将伪码捕获等价为假设检验问题,利用局部最佳检测算法推导出乘性噪声环境下的伪码捕获检测统计量,文中给出了基于局部最佳检测算法的捕获结构,并与传统的平方和检测器进行了性能仿真对比,结果表明该文所提出的捕获方法在乘性噪声环境下检测性能较平方和检测器有较大幅度的提高,而在无乘性噪声的环境下检测性能只较传统的平方和检测器检测性能稍有降低。     

双模非高斯噪声背景下级联双稳态随机共振信号检测

针对随机共振(SR)以高斯噪声为研究背景的局限性,为了分析非高斯噪声对级联随机共振的影响且验证在双模非高斯噪声中级联随机共振的降噪及波形整形特性的可靠性,提出了级联双稳随机共振系统在双模非高斯情况下的微弱信号检测方法。输入信号在进行信号检测过程中,首先把概率密度函数作为随机共振现象的衡量指标,然后当系统处于最佳随机共振状态时,分析了非高斯参数、相关时间及噪声强度之间的关系。最后通过仿真证实,与一级SR相比,二级SR的噪声强度和相关时间的可用范围随着非高斯参数的减少不仅会得到增大,而且滤波特性、信号检测效果得到明显提高。     

乘性噪声环境下基于符号秩统计量的非参伪码捕获方法

扩频系统常常工作在多径环境中,伴随着加性噪声的同时往往还存在着乘性噪声.该文提出了一种乘性噪声环境下基于符号秩统计量的非参伪码捕获方法,将伪码捕获等价为假设检验问题,利用局部最佳检测算法推导出了乘性噪声环境下基于符号秩的检测统计量,通过简化记分函数进一步给出了局部次佳秩检测器.将局部次佳秩检测器与局部最佳检测器和平方和检测器的性能进行了仿真对比,结果表明该文所提出的捕获方法在乘性噪声环境下检测性能接近于乘性噪声环境下的局部最佳检测器,而较平方和检测器则有较大幅度的提高.     

乘性和加性噪声中复谐波恢复的乘积型循环统计量方法

本文提出了乘积型循环统计量的概念,利用这一概念讨论了乘性和加性平稳噪声条件下的谐波恢复问题。首先从理论上证明了循环均值的样本估计误差服从零均值渐近正态分布,提出采用乘积型循环均值来抑制噪声,并给出了基于乘积型循环统计量的谐波估计的具体方法,仿真实验验证了该算法的有效性。     

乘性和加性噪声中谐波恢复的循环统计量方法

本文从循环平衡的观点出发来研究乘性和加性噪声中的谐波恢复问题.首先，在一定条件下建立了一般复过程的有限长付里叶变换的大样本性质．然后，得到了任意阶循环矩的样本估计关于循环频率的一致收敛速度．对于乘性和加性噪声中的谐波信号，建立了一、二、三阶循环矩样本估计的统计性质．在此基础上，分别提出了基于不同阶循环矩的谐波分最个数和频率的估计方法，并得到了估计的强相容性质和强收敛速度，最后给出了模拟实验结果．     

乘性和加性噪声中二维谐波频率估计

噪声中的谐波恢复问题是信号处理领域的一个典型问题,在众多领域中有着广泛的应用。本文主要研究零均值乘性和加性噪声并存下的二维谐波信号频率估计问题,提出了一种基于数据矩阵的奇异值分解和子空间的旋转不变性的零均值乘性和加性噪声中的谐波频率的估计方法。乘性噪声为零均值情形下传统的估计方法往往难以直接应用或估计失效。本文利用谐波模型信号特征,通过对观测信号进行平方运算构造了一个数据矩阵。通过对数据矩阵的特征值进行理论分析,结合子空间旋转不变性,得到了零均值乘性和加性噪声中的谐波频率和数据矩阵之间的一种内在关系。这个性质可以用于零均值乘性和加性噪声并存下的二维谐波信号频率估计,并且所得的二维频率能自动配对。仿真实验验证了本文所提算法的有效性。      

随机共振理论在微弱信号检测中的应用研究

本文介绍了随机共振的基本原理,通过数值仿真研究了对称双稳系统的随机共振现象,并将其用于微弱信号的检测。为了进一步提高检测信噪比,采用了系统参数寻优的方法。仿真结果证明了基于随机共振的微弱信号检测方法的有效性。最后,提出了几个需要解决的问题。     

基于二维循环统计量的二维谐波信号参数估计

本文利用二维循环统计量方法对二维平稳乘性噪声与二维平稳加性噪声共存情况下的二维谐波信号参数估计问题进行了讨论.利用二维循环统计量能够有效地抑制二维平稳乘性噪声和二维平稳加性噪声的特点,有效地从噪声中提取出信号参数.仿真实验对算法作了说明.     

乘性和加性噪声相关背景下的二维谐波频率估计

本文利用二维循环统计量方法对乘性噪声之间相关、乘性噪声和加性噪声之间也相关这种复杂噪声背景下的谐波恢复问题进行了研究.首先,提出了二维噪声互可混的概念,用它来体现多个二维噪声之间的关系;然后,在乘性噪声为非零均值时,定义了二维循环均值来估计信号频率.在乘性噪声和加性噪声为零均值时,定义特殊的二维六阶时间平均多矩谱切片来估计信号频率.仿真实验证明了算法的有效性.     

On the error probability of linearly modulated signals on Rayleighfrequency-flat fading channels

Consideration is given to optimal detection of linearly modulated signals subject to multiplicative Rayleigh-distributed distortion and additive white Gaussian noise. For coherent detection, regenerated amplitude and phase references are employed at the receiver to compensate for amplitude and phase deviations from the correct values. A system model is formulated under the assumption of perfect symbol timing and in the absence of intersymbol interference, producing a final additive noise term, applied just before the detection, which contains the effects of the original additive and multiplicative distortions and of the errors in the phase and amplitude references. By determining the probability density function of this final noise term for arbitrary types of linear modulation, it is possible to perform exact calculations of error probabilities     

Multi-frame super-resolution reconstruction based on mixed Poisson–Gaussian noise

In this paper, a non-blind multi-frame super-resolution (SR) model based on mixed Poisson–Gaussian noise (MPGSR) is proposed. Poisson noise arises from the stochastic nature of the photon-counting process. Readout noise and reset noise inherent to the readout circuitry can be modeled by an additive Gaussian noise. Therefore, a mixed Poisson–Gaussian noise model is more appropriate for real imaging system. Instead of deriving the data fidelity term from the perspective of error norms and the corresponding influence functions, we address the multi-frame SR problem based on a statistical noise model. The derived objective function is decomposed into sub-functions and solved by the alternating direction method of multipliers (ADMM) algorithm which allows using techniques of constrained optimization. The validation of the proposed MPGSR was performed quantitatively and qualitatively on natural and X-ray images. In comparison to the optimization-based and learning-based state-of-the-art methods, we have demonstrated the feasibility of MPGSR and the significance of applying a more appropriate noise model on the SR image reconstruction.     

Improved MLV filter to remove multiplicative noise

More methods can be used to remove the additive noise, such as the Mean of Least Variance （MLV） filter. When the signal is noised by the multiplicative noise, it is difficult to remove. The paper presents an improved filter to remove multiplicative noise by changing the multiplicative noise to the additive noise, and then using the MLV-like to remove the additive noise. The simulation results show that the performance is better than Minimum Coefficient of Variation （MCV） filter and MLV filter. Both one-dimension and image experiments demonstrate its theoretical performance.     

自适应正则化项去除乘性噪声

针对乘性噪声去噪算法研究,乘性噪声多出现在合成孔径雷达、超声波和激光等相干图像系统中,与标准高斯加性噪声不同,乘性噪声符合瑞利和伽马分布函数。文中通过:(1)取对数把乘性噪声模型转变成相加形模型;(2)改进正则化项成为自适应扩散模型;(3)将数学模型应用于图像处理的实践当中。在解决了阶梯效应的同时保持了图像的边缘。     

复杂噪声中的二维DOA估计新方法

基于二维DOA估计模型,本文研究了复杂环境——乘法与加法观测噪声共存时的DOA估计问题.针对工程实践中广泛存在的循环平稳信号,提出了一种采用循环统计量的新方法.该方法对任何平稳的加性与乘性噪声均不敏感,因而具有较大的实用性.