基于群Monte Carlo的大气噪声二维模型参数估计

为解决多天线最佳接收下的多维非高斯噪声参数估计问题,提出了基于群蒙特卡洛的大气噪声二维模型参数估计方案,通过联合设计蒙特卡洛马尔科夫链和优化重要性重采样算法,实现噪声模型的全局最优参数估计。针对该算法高强度运算需求,在GPU平台上对核心运算作细粒度并行计算处理并优化设计,使运算速度大幅提升,以满足实时处理要求。仿真实验结果表明,该算法迭代收敛快,精度高,各参数估计相对误差普遍小于0.02,最大相对误差可控制在0.05以内,运算速度较传统计算有大幅度的提高,可充分满足低频通信系统中实时计算的要求。

双树轮廓波变换域的磁共振图像降噪

为了改善磁共振(MR)图像的质量,提出一种基于双树轮廓波(DT-Contourlet)变换的MR图像降噪算法。研究了MR图像的噪声分布模型,认为这种噪声服从莱斯分布,从而推导了MR模平方图像的噪声参数估计方法。通过分析DT-Contourlet的塔型双树方向滤波器组结构,明确了DT-Contourlet不仅能保持轮廓波灵活的方向选择性,而且克服了传统轮廓波不具有平移不变性的缺点。在DT-Contourlet变换域,通过计算方差一致性测度,用局部自适应窗口估计阈值萎缩因子,对MR模平方图像的变换系数进行阈值萎缩。最后,经过DT-Contourlet反变换,实现了MR图像的降噪处理。实验结果表明,用本文算法降噪的MR仿真图像的峰值信噪比(PSNR)优于传统算法;与基于小波和轮廓波的方法相比,不同噪声方差下的PSNR平均提高了2.13dB和0.91dB。从视觉效果来看,该算法能在有效抑制MR图像噪声的同时,更好地保持图像的细节信息。

一种基于目标提取的空间观测图像预处理算法

在深入分析空间观测图像中,像素灰度在空间和帧间分布特征的基础上,提出一种基于目标提取的空间观测图像预处理算法,算法包括:噪声分布参数估计、盲元定位与补偿、图像降噪、图像分割以及目标提取等步骤。实验结果表明,该算法能够抑制不均匀星空背景,提取观测图像中的恒星星点和卫星星点,同时使图像信噪比在预处理完成之后有所提高。

A Cross-Reference Method for Nonlinear Time Series Analysis in Semi-Blind Case

In this paper, we propose a cross reference method for nonlinear time series analyzing in semi blind case, that is, the dynamic equations modeling the time series are known but the corresponding parameters are not. The tasks of noise reduction and parameter estimation which were fulfilled separately before are combined iteratively. With the positive interaction between the two processing modules, the method is somewhat superior. Some prior work can be viewed as special cases of this general framework. The simulations for noise reduction and parameter estimation of contaminated chaotic time series show improved performance of our method compared with previous work.

Unbiased parameter estimation of continuous-time system based on modulating functions with input and output white noises

An efficient unbiased estimation method is proposed for the direct identification of linear continuous-time system with noisy input and output measurements.Using the Gaussian modulating filters,by numerical integration,an equivalent discrete identification model which is parameterized with continuous-time model parameters is developed,and the parameters can be estimated by the least-squares (LS) algorithm.Even with white noises in input and output measurement data,the LS estimate is biased,and the bias is determined by the variances of noises.According to the asymptotic analysis,the relationship between bias and noise variances is derived.One equation relating to the measurement noise variances is derived through the analysis of the LS errors.Increasing the degree of denominator of the system transfer function by one,an extended model is constructed.By comparing the true value and LS estimates of the parameters between original and extended model,another equation with input and output noise variances is formulated.So,the noise variances are resolved by the set of equations,the LS bias is eliminated and the unbiased estimates of system parameters are obtained.A simulation example by comparing the standard LS with bias eliminating LS algorithm indicates that the proposed algorithm is an efficient method with noisy input and output measurements.

Iterative identification of output error model for industrial processes with time delay subject to colored noise

To deal with colored noise and unexpected load disturbance in identification of industrial processes with time delay, a bias-eliminated iterative least-squares(ILS) identification method is proposed in this paper to estimate the output error model parameters and time delay simultaneously. An extended observation vector is constructed to establish an ILS identification algorithm. Moreover, a variable forgetting factor is introduced to enhance the convergence rate of parameter estimation. For consistent estimation, an instrumental variable method is given to deal with the colored noise. The convergence and upper bound error of parameter estimation are analyzed. Two illustrative examples are used to show the effectiveness and merits of the proposed method.

Nonparametric VSS-APA based on precise background noise power estimate

The adaptive algorithm used for echo cancellation(EC) system needs to provide 1) low misadjustment and 2) high convergence rate. The affine projection algorithm(APA) is a better alternative than normalized least mean square(NLMS) algorithm in EC applications where the input signal is highly correlated. Since the APA with a constant step-size has to make compromise between the performance criteria 1) and 2), a variable step-size APA(VSS-APA) provides a more reliable solution. A nonparametric VSS-APA(NPVSS-APA) is proposed by recovering the background noise within the error signal instead of cancelling the a posteriori errors. The most problematic term of its variable step-size formula is the value of background noise power(BNP). The power difference between the desired signal and output signal, which equals the power of error signal statistically, has been considered the BNP estimate in a rough manner. Considering that the error signal consists of background noise and misalignment noise, a precise BNP estimate is achieved by multiplying the rough estimate with a corrective factor. After the analysis on the power ratio of misalignment noise to background noise of APA, the corrective factor is formulated depending on the projection order and the latest value of variable step-size. The new algorithm which does not require any a priori knowledge of EC environment has the advantage of easier controllability in practical application. The simulation results in the EC context indicate the accuracy of the proposed BNP estimate and the more effective behavior of the proposed algorithm compared with other versions of APA class.

Improved polyreference time domain method for modal identification using local or global noise removal techniques

Modal identification involves estimating the modal parameters, such as modal frequencies, damping ratios, and mode shapes, of a structural system from measured data. Under the condition that noisy impulse response signals associated with multiple input and output locations have been measured, the primary objective of this study is to apply the local or global noise removal technique for improving the modal identification based on the polyreference time domain (PTD) method. While the traditional PTD method improves modal parameter estimation by over-specifying the computational model order to absorb noise, this paper proposes an approach using the actual system order as the computational model order and rejecting much noise prior to performing modal parameter estimation algorithms. Two noise removal approaches are investigated: a "local" approach which removes noise from one signal at a time, and a "global" approach which removes the noise of multiple measured signals simultaneously. The numerical investigation in this article is based on experimental measurements from two test setups: a cantilever beam with 3 inputs and 10 outputs, and a hanged plate with 4 inputs and 32 outputs. This paper demonstrates that the proposed noise-rejection method outperforms the traditional noise-absorption PTD method in several crucial aspects.