二维谐波频率估计的高精度稳健迭代算法

本文运用基于两阶段的稳健迭代算法考虑了加性噪声中二维多分量谐波频率参数的估计问题。可以证明该算法仅需要三次迭代就能达到收敛,且最终估计量达到和最小二乘估计（LSE）相同的收敛速度以及估计方差达到Cramer-Rao下界。由于基于统计量的迭代估计充分利用了谐波模型的内在特性以及噪声分布特性,所以仅需要三步迭代就达到收敛,因而使得算法计算量很小且稳定,另外还可以证明三步迭代之后的估计量为二维谐波频率的无偏以及一致估计。本文证明了[9]中的单分量模型下的迭代算法可用于多分量情形下的估计,并且本文所采用的频率参数的平行估计方式避免了[9]中逐个估计方式下前一个已估计频率对分量对后一个要估计频率对分量的影响。最后模拟实验证实了估计的无偏性和一致性以及估计量在中样本情形下具备很高的估计精度。      

基于子空间旋转不变性的加性有色噪声中谐波频率的估计

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。本文主要研究加性有色噪声中谐波频率的估计问题,提出了一种基于子空间旋转不变性的谐波频率的高分辨率估计方法。利用观测信号的自协方差函数构造了一个协方差矩阵,通过对协方差矩阵的特征值进行理论分析,结合子空间旋转不变性,得到了加性有色噪声中谐波的频率和协方差矩阵之间的一种内在联系。利用这个性质可以估计加性有色噪声中谐波的频率。本文方法对于有色噪声的模型无任何假设,而且对于噪声的分布也没有限制,对于高斯和非高斯有色噪声都适用。仿真实验验证了本文所提算法的有效性。      

有色噪声中谐波频率的频域非线性预滤波估计方法

基于谐波信号表现为频域中的 异常点 这一思路,利用滑动中值滤波器滤除数据异常点的能力,本文提出了未知有色噪声背景下谐波信号频率的一种高分辨估计方法.其突出特点是对噪声分布的强普适性,且算法实现简单.仿真结果验证了该方法的性能.     

非高斯有色噪声背景下二维谐波频率估计的累积量投影方法

针对非高斯有色噪声中的二维谐波频率估计问题,该文提出了复数线性非高斯过程的二维累积量投影定理。应用该定理并巧妙地构造观测信号的高阶累积量求得非高斯噪声的自相关,并通过求解一个广义特征值对噪声空间进行预白化,然后结合高分辨率的子空间方法二维MUSIC估计得到二维谐波参量。该文方法解决了非高斯有色噪声中的二维谐波频率估计问题,特别地当非高斯噪声为对称分布和谐波信号中存在二次相位耦合时该文方法同样有效。仿真实验验证了该文结论。     

高斯白噪声中单频复正弦信号频率估计

本文提出了一种在加性复高斯白噪声中对单频复正弦信号频率的估计方法。该方法是基于利用自相关系数对Kay的方法和扩展。它从自相关系数的相位差的最佳线性组合中提取频率值。相对于Kay的方法，自相关系数中相位差信息的信噪比提高了。新的估计方法的方差具有更低的输入信噪经阈值，而保持了Kay的方法的无偏性和频率估计范围。我们给出了新方法的均方误差分析及其数值结果。该方差分析适用于中等以上的信噪比或大样本长度的情况。给出了估计其估计方差输入信噪比阈值的近似公式，该公式得出的阈值与样本长度大于24的模拟结果相当接近。     

非高斯有色噪声中的正弦信号频率估计

本文研究非高斯ＡＲＭＡ有色噪声中的正弦信号频率估计问题。利用自相关函数和三阶累积量相结合，提出了一种先估计噪声模型ＡＲ参数，然后对观测值进行预滤波，最后估计信号模型参数的新方法，模拟实验结果表明，新方法具有良好的频率估计性能。     

基于细化频谱的频率迭代插值估计算法

在加性高斯白噪声环境的单频复指数信号频率估计中,针对现有频率估计算法估计误差分布不均且估计精度较低的问题,该文提出一种细化频谱迭代插值估计算法。该算法首先根据半长信号的快速傅里叶变换峰值位置计算细化频谱,再利用细化频谱幅值进行频率的无偏插值估计,最后利用估计结果和全长信号更新细化频谱并进行迭代插值重估。仿真结果表明,与现有算法相比,所提算法估计误差分布均匀,且在高信噪比的所有频率范围和低信噪比的大部分频率范围内估计精度更高。同时仿真表明,所提算法在加性均匀噪声环境中也有较好的估计性能。     

实时高精度频率估计算法

提出一种高精度频率估计算法,即在低信噪比（ＳＮＲ）下降短时直接中频采样数据分段,求其平均幅度谱粗测频率,再加权其相位精确估计信号的频率,以便获得较高的测频精度。估计误差接近Ｃｒａｍｅｒ－Ｒａｏ（ＣＲ）下界,并且能够有效地分离同时达到的多个信号。     

一种高精度频率估计的抗噪声调幅干扰算法

噪声调幅干扰对消中,干扰频率估计精度较低将导致干扰对消效果严重退化.针对此问题,提出一种基于高精度干扰频率估计的噪声调幅干扰对消方法.该方法首先对Candon算法和Rife算法中谱线的幅值作线性融合处理,然后基于内插离散傅里叶变换,分割离散频谱区域,最后对干扰信号解调并利用频域对消方法抑制干扰.仿真结果表明,该方法具有良好的频率估计性能并能在强干扰背景下有效地抑制噪声调幅干扰.     

Algorithm for Estimating Type and Probability of Impulse Noise in Color Images

Journal of Communications Technology and Electronics - Abstract—Color digital images are often contaminated by impulse noise during transmitting and recording of information. Efficient...     

Frequencies Estimation of Two Dimensional Harmonics in Additive Noise with Quaternion

1 Introduction In many applications ,such as radar ,sonar and com-munication, parameters esti mation of two di mensionalharmonics was veryi mportant .In order to get highres-olutions esti mation, many subspace methods of one di-mensional harmonics , such as MUSIC, MEMP,ES-PRIT, have been developed to esti mate parameters oftwo di mensional harmonics[1 ~3]. When additive noise was colored Gaussian noiseH.M.ibrahi m[4]and R. R. Gharieb[5]analyzed thisproblem with high-order cumulants sin…     

An Orthogonal Design of Experiments for Accurately Estimating Harmonics of Periodic Signals at Low Frequencies

The lack of synchronization between the sampling rate and the signal frequency is the main source of leakage errors in the harmonic analysis of periodic signals performed by means of digital techniques. An algorithm for accurately measuring the harmonic parameters of low-frequency, arbitrary voltage signals without using synchronization circuits was published recently. It is shown that the algorithm is an alternative orthogonal design of experiments for the problem of fitting a linear trigonometric model to integrating digital voltmeter data. This was experimentally confirmed in recent comparison of the proposed method with a synchronous synthesizing and sampling system. The harmonic magnitudes as a percentage of the fundamental measured by both methods differ by less than one part in 10^{6}. The algorithm can be advantageously used in almost any kind of low-frequency ac applications where two arbitrary voltage signals measured by two voltmeters are to be compared for harmonic magnitude and phase shift.     

基于遗传算法的复杂背景噪声中二维谐波的二次耦合分析

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中二维谐波的二次非线性耦合问题做了分析。利用当且仅当二维谐波中存在二次非线性耦合谐波时,所定义的二维三阶时间平均矩谱仅在参与二次耦合谐波频率处取得极值的性质,提出了采用遗传算法来分析二维谐波的：二次非线性耦合问题的算法。仿真实验证明了算法的有效性。     

An Iterative Bayesian Algorithm for Sparse Component Analysis in Presence of Noise

We present a Bayesian approach for sparse component analysis (SCA) in the noisy case. The algorithm is essentially a method for obtaining sufficiently sparse solutions of underdetermined systems of linear equations with additive Gaussian noise. In general, an underdetermined system of linear equations has infinitely many solutions. However, it has been shown that sufficiently sparse solutions can be uniquely identified. Our main objective is to find this unique solution. Our method is based on a novel estimation of source parameters and maximum a posteriori (MAP) estimation of sources. To tackle the great complexity of the MAP algorithm (when the number of sources and mixtures become large), we propose an iterative Bayesian algorithm (IBA). This IBA algorithm is based on the MAP estimation of sources, too, but optimized with a steepest-ascent method. The convergence analysis of the IBA algorithm and its convergence to true global maximum are also proved. Simulation results show that the performance achieved by the IBA algorithm is among the best, while its complexity is rather high in comparison to other algorithms. Simulation results also show the low sensitivity of the IBA algorithm to its simulation parameters.     

Robust Recursive Inverse Adaptive Algorithm in Impulsive Noise

The recently proposed Recursive Inverse (RI) algorithm has shown a significant performance improvement compared to that of the Recursive Least Squares (RLS) algorithm, in various noise environments. However, both algorithms fail to converge in certain impulsive noise environments, especially if the Signal-to-Noise Ratio (SNR) is low. In this paper, a Robust RI algorithm is proposed. Analytical results show that robustness against impulsive noise is achieved by choosing the weights on the basis of the L ₁ norms of the autocorrelation matrix and the cross-correlation vector. Simulation results confirm that the proposed algorithm provides an improved performance, with a reduction in computational complexity, compared to those of the RLS and the Robust RLS in white and correlated impulsive noise.     

噪声背景下一种正弦信号频率估计的新插值算法

本文研究噪声中正弦信号频率的估计问题.结合DFT变换和插值方法,提出了一种基于DFT变换系数的频率估计算法,其计算简单.结果表明,该方法有效地改善了频率估计精度,且便于工程实现.     

Alpha稳定分布噪声中的韧性投影 近似子空间跟踪算法

 为了克服投影近似子空间跟踪算法(PAST)在脉冲噪声环境下性能的退化,本文以Alpha 稳定分布为脉冲噪声模型,依据韧性的M估计方法提出了一种新的代价函数,并推导出基于递归最小M估计的韧性投影近似自适应信号子空间跟踪算法(RLM_-PAST).由于采用了适合噪声模型的M估计函数,新算法与采用递归最小二乘估计的子空间跟踪算法相比,在稳定分布脉冲噪声环境下具有更好的性能.把新方法应用于波达方向(DOA)估计,数值仿真结果表明了该算法的有效性.     

频分MIMO雷达分解迭代稳健自适应波束形成算法

频率分集雷达因其独特的距离依赖波束特性而受到广泛关注。针对频率分集雷达稳健波束形成问题,本文建立了二维频分子孔径MIMO(FDS-MIMO)雷达阵列信号模型,理论导出了基于等效载频的权向量分解解析解,提出了一种分解迭代的稳健自适应波束形成算法。为解决距离依赖波束栅瓣导致的周期性输出信干燥比(SINR)损失的问题,进一步提出了一种沿平面阵两方向互质的频率偏置方案。仿真结果表明,与传统算法相比在导向矢量存在失配的情况下,本文所提方法能够有效抑制输出SINR周期性损失,且具有计算复杂度低,训练样本需求少,抗导向误差失配稳健性强等优点。      

乘法噪声中非线性耦合谐波分析

针对信号的非平稳性质,引入傅里叶级数多谱和比例多周期图的概念,分别提出了零均值乘法噪声二次耦合及非零均值乘法噪声三次耦合谐波频率估计方法,仅使用单个数据记录即可得到耦合频率估计,并且可以抑制加性平稳噪声而无须假定其颜色和分布.最后给出了仿真实验结果.