毫米波/红外成像系统中的精确极大似然误差估计算法

为精确估计毫米波雷达/红外成像复合系统中传感器的系统误差,提出了一种基于无偏转换测量的精确极大似然(UCM-EML)误差估计算法.根据极坐标系下的测量噪声建立误差估计模型,据此推导似然函数和准则函数,采用高斯-牛顿迭代法进行准则函数的优化.仿真实验结果表明,UCM-EML算法在误差估计精度和收敛速度上都优于精确极大似然估计算法和修正的精确极大似然算法.     

基于小波分析的分形参数估计新方法

该文研究目的是估计1/f类分形随机过程参数矢量 (, 2,2w)。作者基于小波分析,对1/f过程观测值的小波系数方差进行一系列代数运算,并给出详尽的证明过程,最终求取了噪声中分数布朗运动(fBm)参数矢量的估计量。实验结果表明,与传统的极大似然估计(ML)相比,算法简洁,效果良好,估计参数范围广泛,同时对噪声也不再局限于高斯分布。     

数字通信前馈算法中的最大似然同步算法仿真

快速精确地实现传输时延的估计是突发数字通信中的研究重点和难点。研究了数字通信前馈算法中的最大似然同步算法,对最大似然同步算法的原理进行了分析,根据最大似然函数的不同,推导出4种不同的最大似然估计算法,并以QPSK调制方式为例,在Matlab环境下对4种算法进行了仿真,估计了各种因素对算法的影响。仿真结果表明,最大似然同步算法的估计精度高,估计范围广,满足突发通信的要求。     

基于无线传感器网络的声目标定位算法研究

在建立声音能量模型基础上实现了基于极大似然估计的声目标定位。由于极大似然估计算法是一个有偏估计,而且容易受到参数扰动的影响,这两个不足影响了其定位精度的稳定性。因此,提出了基于最优化极大似然估计的目标定位算法。仿真实验结果表明,与一些存在的定位算法相比,此算法在传感器节点数目不同时,都能得到更高的定位精度。     

基于马尔可夫随机场的图像去噪复原方法

由于样本空间的多样性,势函数模型难以计算,因此无法得到马尔可夫随机场模型的参数估计。针对该问题,提出基于麦克劳林级数的马尔可夫随机场参数估计算法。通过二阶麦克劳林级数的展开式得到了势函数的近似值和似然函数的表示式,推导出极大似然估计对应的非线性方程组,通过牛顿迭代法得到方程组的解即是马尔可夫随机场的极大似然估计。提出了一种改进的Gibbs采样方法,加快了模拟退火的速度。实验分别从视觉效果、峰值信噪比和稳态迭代次数三方面验证了算法的有效性。     

基于先验信息和正则化技术的图像复原算法的研究

在湍流退化图像复原研究中,为了消除大气湍流的影响,提出了一种基于先验信息和正则化技术的盲解卷积图像复原算法.该算法是以极大似然估计为基本原理,将目标图像和点扩展函数的先验信息以惩罚项的形式引入到极大似然函数中,同时利用正则化技术优化目标图像和点扩展函数的估计过程,以增加极大似然估计算法的收敛性和稳定性.通过退化图像的复原实验结果表明,该算法在退化模型完全未知的情况下,可以有效的实现对湍流退化图像的盲复原.     

半导体器件并联系统可靠性评估的经验Bayes方法

为了提高机载开关电源中半导体器件并联系统可靠性评估的准确性,运用经验 Bayes 法和经典的统计方法,研究了该系统的可靠性评估问题。分别给出了系统可靠性指标的经验 Bayes 估计,极大似然估计。利用 Monte-Carlo方法,对两种估计结果进行了比较,结果表明,经验 Bayes 估计的最大绝对误差为 0.07,它小于极大似然估计的最大绝对误差 0.368。     

基于混沌自适应变异粒子群优化的解相干算法

针对相干信源波达方向估计的需要,结合粒子群优化算法,论文提出了一种基于混沌自适应变异粒子群优化的广义极大似然算法(CAMPSOGML),算法对阵列的几何结构没有任何约束,分辨的信源数可大于阵元数,算法把混沌初始化和自适应变异策略引进粒子群算法中,有效地提高了收敛速度,克服了粒子群算法容易陷入局部最优值的缺点。计算机仿真表明：与基于实数遗传算法和粒子群算法的广义极大似然估计方法相比,CAMPSOGML算法在收敛速度和估计精度上都有优势,是一种新颖的有效的解相干算法。     

基于最大似然估计的16APSK载波捕获算法研究

针对在低信噪比情况下16APSK信号频偏捕获困难的问题,讨论了一种结合Kalman滤波和最大似然估计的非数据辅助频偏估计算法。该算法对包含载波频偏信息的相位序列进行最大似然频偏估计,通过Kalman滤波,降低了噪声对估计精度的影响。对结合Kalman滤波和最大似然估计的16APSK频偏估计算法进行了仿真。仿真结果表明,在低信噪比条件下,结合Kalman滤波的最大似然频偏估计算法可获得良好的性能。     

基于MUSIC和ML方法的MIMO系统参数估计

该文提出了一种基于MUSIC和ML方法联合估计MIMO系统频偏和信道增益的算法,该算法首先使用MUSIC方法估计出多个发射天线到某一接收天线的频偏子集,然后利用最大似然方法在这个有限子集中分离出不同天线对之间的频偏,最后在频率同步的基础上利用最大似然估计器对信道增益进行估计。该算法解决了在估计多个频偏时直接使用最大似然估计进行多维搜索的问题,将多维搜索转化为一维搜索,降低了算法的复杂度。     

Mutual Information for Stochastic Signals and Fractional Brownian Motion

The mutual information between a stochastic signal and this signal plus a fractional Brownian motion (described as an additive fractional Gaussian noise channel) is expressed as the error of an estimation problem that can be naturally associated with this model. If the stochastic signal with the additive fractional Brownian motion occurs multiplied by a scalar parameter, then the rate of change of the mutual information with respect to this parameter is described by the error of another related estimation problem. These results generalize some results for a model where the fractional Brownian motion is a Brownian motion to a model with an arbitrary fractional Brownian motion.      

Fractal feature analysis and classification in medical imaging

Following B.B. Mandelbrot's fractal theory (1982), it was found that the fractal dimension could be obtained in medical images by the concept of fractional Brownian motion. An estimation concept for determination of the fractal dimension based upon the concept of fractional Brownian motion is discussed. Two applications are found: (1) classification; (2) edge enhancement and detection. For the purpose of classification, a normalized fractional Brownian motion feature vector is defined from this estimation concept. It represented the normalized average absolute intensity difference of pixel pairs on a surface of different scales. The feature vector uses relatively few data items to represent the statistical characteristics of the medial image surface and is invariant to linear intensity transformation. For edge enhancement and detection application, a transformed image is obtained by calculating the fractal dimension of each pixel over the whole medical image. The fractal dimension value of each pixel is obtained by calculating the fractal dimension of 7x7 pixel block centered on this pixel.     

Estimation of 2-D noisy fractional Brownian motion and itsapplications using wavelets

The two-dimensional (2-D) fractional Brownian motion (fBm) model is useful in describing natural scenes and textures. Most fractal estimation algorithms for 2-D isotropic fBm images are simple extensions of the one-dimensional (1-D) fBm estimation method. This method does not perform well when the image size is small (say, 32x32). We propose a new algorithm that estimates the fractal parameter from the decay of the variance of the wavelet coefficients across scales. Our method places no restriction on the wavelets. Also, it provides a robust parameter estimation for small noisy fractal images. For image denoising, a Wiener filter is constructed by our algorithm using the estimated parameters and is then applied to the noisy wavelet coefficients at each scale. We show that the averaged power spectrum of the denoised image is isotropic and is a nearly 1/f process. The performance of our algorithm is shown by numerical simulation for both the fractal parameter and the image estimation. Applications to coastline detection and texture segmentation in a noisy environment are also demonstrated.     

Fractional brownian motion: a maximum likelihood estimator and its application to image texture

Fractals have been shown to be useful in characterizing texture in a variety of contexts. Use of this methodology normally involves measurement of a parameter H, which is directly related to fractal dimension. In this work the basic theory of fractional Brownian motion is extended to the discrete case. It is shown that the power spectral density of such a discrete process is only approximately proportional to |f|a instead of in direct proportion as in the continuous case. An asymptotic Cramer-Rao bound is derived for the variance of an estimate of H. Subsequently, a maximum likelihood estimator (MLE) is developed to estimate H. It is shown that the variance of this estimator nearly achieves the minimum bound. A generation algorithm for discrete fractional motion is presented and used to demonstrate the capabilities of the MLE when the discrete fractional Brownian process is contaminated with additive Gaussian noise. The results show that even at signal-to-noise ratios of 30 dB, significant errors in estimation of H can result when noise is present. The MLE is then applied to X-ray images of the human calcaneus to demonstrate how the line-to-line formulation can be applied to the two-dimensional case. These results indicate that it has strong potential for quantifying texture.     

A maximum likelihood estimate for two-variable fractal surface

The fractal dimension estimate for two-variable fractional Brownian motion using the maximum likelihood estimate (MLE) is developed. We formulate a model to describe the two-variable fractional Brownian motion, then derive the likelihood function for that model and estimate the fractal dimension by maximizing the likelihood function. We then compare the MLE with the box-dimension estimation method.     

Extending self-similarity for fractional Brownian motion

The fractional Brownian motion (fBm) model has proven to be valuable in modeling many natural processes because of its persistence for large time lags. However, the model is characterized by one single parameter that cannot distinguish between short- and long-term correlation effects. This article investigates the idea of extending self-similarity to create a correlation model that generalizes discrete fBm referred to as asymptotic fBm (afBm). Namely, afBm is parameterized by variables controlling short- and long-term correlation effects. It proposes a fast parameter estimation algorithm for afBm based on the Haar transform, and demonstrates the performance of this parameter estimation algorithm with numerical simulations     

基于H.264的内容自适应分数像素运动估计算法

分数像素精确运动估计的改进是整个运动估计模块优化的关键,本文提出了基于H.264的内容自适应分数像素运动估计算法。首先,提出基于平坦区域宏块预测的无效分数像素运动矢量(MV)搜索省略算法(SMBP);然后,改进H.264采用的基于中心的分数像素搜索算法(CBFPS),提出基于预测矢量的增强型菱形模板(EDSP)搜索算法。实验结果表明,内容自适应分数像素运动估计算法比分数像素全搜索算法(FFPS)在峰值信噪比(PSNR)有微小降低(0.095～0.209 dB)的情况下,平均减少了75.6%的分数像素搜索点,整个运动估计模块平均节省了38.5%的计算量。     

小数像素运动估计快速算法

高精度的匹配和补偿可以减少预测误差，提高压缩视频图像的质量。提出了一种小数像素精度视频运动估计的快速算法，根据半像素精度运动估计的中间结果，直接推算出最高精度的运动估计。根据该算法可以快速得到任意小数像素精度的运动估计。     

Estimation of fractional Brownian motion embedded in a noisyenvironment using nonorthogonal wavelets

We show that nonorthogonal wavelets can characterize the fractional Brownian motion (fBm) that is in white noise. We demonstrate the point that discriminating the parameter of fBm from that of noise is equivalent to discriminating the composite singularity formed by superimposing a peak singularity on a Dirac singularity. We characterize the composite singularity by formalizing this problem as a nonlinear optimization problem. This yields our parameter estimation algorithm. For fractal signal estimation, Wiener filtering is explicitly formulated as a function of the signal and noise parameters and the wavelets. We show that the estimated signal is a 1/f process. Comparative studies through numerical simulations of our methods with those of Wornell and Oppenheim (1992) are presented     

A fast estimation algorithm on the Hurst parameter of discrete-timefractional Brownian motion

The purpose of this paper is to demonstrate that the discrete-time fractional Gaussian noise (DFGN) is a regular process. Based on this property, a fast algorithm with low computational cost is proposed to estimate the Hurst parameter H, which is an important parameter in fractional Brownian motion (FBM). This algorithm is robust under amplitude shift, invariant to time shift, and unaffected by a scaling factor in power spectral density (PSD). Finally, the computational complexity is also investigated