乘性和加性噪声中二维谐波的参数估计

本文从循环平稳的观点出发来研究乘性和加性噪声中二维谐波的参数估计问题,利用循环累积量的性质证明了二维二阶循环累积量单一记录估计的统计性质.在此基础上,对于零均值乘性噪声中的二维谐波信号提出了基于二阶循环累积量的二维谐波分量个数和频率对的估计方法,并分别得到了估计的强收敛速度.仿真试验验证了所提方法的性能.     

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.     

乘性噪声背景下基于三阶循环矩的二维近场源定位方法

 本文提出一种基于三阶循环矩的近场源二维参数估计方法.该方法以均匀线阵为接收传感器阵列,通过构造三个三阶循环矩矩阵估计出近场源的方位角和距离参数,利用循环统计量去除加性噪声,通过预校准法消除乘性噪声,适用于乘性噪声或乘性噪声和加性噪声共存背景下的二维近场源定位.仿真实验验证了该方法的有效性.     

脉冲噪声环境下循环ESPRIT新方法

以a稳定分布作为噪声模型,研究了脉冲噪声环境下循环平稳信号的波达方向估计问题.针对在脉冲噪声环境中,基于传统二阶循环统计量的算法效果显著退化的问题,提出了分数低阶循环相关矩阵概念;并在此基础上,提出了分数低阶总体最小二乘(TLS)循环ESPRIT算法的2种形式.计算机仿真表明所提出的算法可有效地估计出脉冲噪声条件下的波达方向,其性能优于传统的基于二阶循环统计量的循环ESPRIT类算法,有潜在的应用前景.     

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。     

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.     

一种基于多循环频率的韧性时延与多普勒频移联合估计算法

本文提出了一种基于分数低阶循环模糊函数的多循环频率时延与多普勒频移联合估计算法.该方法将分数低阶矩与循环平稳特性相结合,能够在SαS(Symmetric α Stable)脉冲噪声条件下检测信号的循环平稳特性.算法充分利用了信号的循环频率信息,具备较强的抑制干扰能力.仿真结果表明,在脉冲噪声和干扰环境中本文提出的算法均...     

乘性和加性噪声中谐波恢复和循环统计量方法

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量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．     

Cramer-Rao bounds and maximum likelihood estimation for randomamplitude phase-modulated signals

The problem of estimating the phase parameters of a phase-modulated signal in the presence of colored multiplicative noise (random amplitude modulation) and additive white noise (both Gaussian) is addressed. Closed-form expressions for the exact and large-sample Cramer-Rao Bounds (CRBs) are derived. It is shown that the CRB is significantly affected by the color of the modulating process when the signal-to-noise ratio (SNR) or the intrinsic SNR is small. Maximum likelihood type estimators that ignore the noise color and optimize a criterion with respect to only the phase parameters are proposed. These estimators are shown to be equivalent to the nonlinear least squares estimators, which consist of matching the squared observations with a constant amplitude phase-modulated signal when the mean of the multiplicative noise is forced to zero. Closed-form expressions are derived for the efficiency of these estimators and are verified via simulations     

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。     

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.     

A multiplicative Nakagami speckle reduction algorithm for ultrasound images

Speckle noise of ultrasound images is of multiplicative nature which degrades the image quality in terms of resolution and contrast. While there exist a number of algorithms for reduction of multiplicative Rayleigh distributed random speckle noise, the low signal-to-noise ratio (SNR) issue of the multiplicative Rayleigh noise is still not adequately resolved. In this paper, a simple 2-dimensional (2D) local intensity smoothing method is presented which transforms the Rayleigh noise contaminated in ultrasound images to Nakagami distributed noise so as to improve the SNR of processed images. A 2D total variation regularized Nakagami speckle reduction algorithm is derived based on the maximum a posteriori estimation framework, which performs well in restoring piecewise-smooth reflectivity and preserving fine details of the image. The proposed algorithm is verified by a series of computer-simulated and real ultrasound image data. It is shown that the algorithm considerably improves the quality of ultrasound images and outperforms the Rayleigh noise based speckle reduction methods in terms of speckle SNR and contrast-to-noise ratio.     

Detection of multiplicative noise in stationary random processesusing second- and higher order statistics

This paper addresses the problem of detecting the presence of colored multiplicative noise, when the information process can be modeled as a parametric ARMA process. For the case of zero-mean multiplicative noise, a cumulant based suboptimal detector is studied. This detector tests the nullity of a specific cumulant slice. A second detector is developed when the multiplicative noise is nonzero mean. This detector consists of filtering the data by an estimated AR filter. Cumulants of the residual data are then shown to be well suited to the detection problem. Theoretical expressions for the asymptotic probability of detection are given. Simulation-derived finite-sample ROC curves are shown for different sets of model parameters     

PN code acquisition using signed-rank-based non-parametric detector in a multiplicative noise model

Multiplicative noise is known to be useful in modelling an environment that is difficult to describe with an additive noise model. In this article, signed-rank-based non-parametric detectors are used for pseudonoise (PN) code acquisition in multiplicative noise. First, a locally optimum (LO) detector based on the signs and ranks of observations is derived, and then the locally suboptimum rank (LSR) detector is proposed by using approximate score functions. The finite sample-size performance of the LSR detector is considered. Numerical results show that the LSR detector asymptotically has almost the same performance as the LO detector for multiplicative noise.     

640×512制冷探测器积分时间与SNR分析

对国产640512中波凝视型制冷焦平面探测器的积分时间与信噪比( SNR)之间的关系进行了研究，以积分时间与探测器输出信号和噪声关系的原理为基础，分别对探测器正常工作时的输出信号情况以及输出噪声情况做了仔细的分析。文中分析了随着积分时间的增加，实际探测器输出信号和噪声信号各自的增长关系，并通过大量的数据和图表印证了这种关系的正确性，并最终得出了积分时间对探测器信噪比的影响程度，给出了最佳工作积分时间。     

Approximate maximum likelihood estimators for array processing inmultiplicative noise environments

We consider the problem of localizing a source by means of a sensor array when the received signal is corrupted by multiplicative noise. This scenario is encountered, for example, in communications, owing to the presence of local scatterers in the vicinity of the mobile or due to wavefronts that propagate through random inhomogeneous media. Since the exact maximum likelihood (ML) estimator is computationally intensive, two approximate solutions are proposed, originating from the analysis of the high and low signal to-noise ratio (SNR) cases, respectively. First, starting with the no additive noise case, a very simple approximate ML (AML₁) estimator is derived. The performance of the AML₁ estimator in the presence of additive noise is studied, and a theoretical expression for its asymptotic variance is derived. Its performance is shown to be close to the Cramer-Rao bound (CRB) for moderate to high SNR. Next, the low SNR case is considered, and the corresponding AML₂ solution is derived. It is shown that the approximate ML criterion can be concentrated with respect to both the multiplicative and additive noise powers, leaving out a two-dimensional (2-D) minimization problem instead of a four-dimensional (4-D) problem required by the exact ML. Numerical results illustrate the performance of the estimators and confirm the validity of the theoretical analysis