色噪声背景下正弦参量估计的互谱矩和SVD方法

互谱估计方法是用于色噪声背景下正弦信号参量估计的一个十分有效的方法。但以往互谱估计都是采用FFT方法和互周期图法。本文首次将现代谱估计方法引入到互谱估计中,从理论上建立了互相关函数的Yule-Walker方程,并在此基础上进而提出了互谱估计的矩估计法和SVD方法。给出了仿真实例结果。     

有色噪声背景下正弦信号频率估计的互谱Pisarenko和MUSIC方法

现代互谱估计是抑制有色噪声的一个有效方法。本文首次对正弦信号的扩阶互相关函数矩阵的特征结构进行了深入的分析，并进而提出了有色噪声背景下正弦信号频率估计的互谱Ｐｉｓａｒｅｎｋｏ方法和ＭＵＳＩＣ方法。     

全相位滤波白噪声产生有色噪声及其功率谱估计

本文提出用全相位数字滤波器这种新型滤波器滤波白噪声产生有色噪声的方法,这种方法比用传统FIR滤波器产生的有色噪声频谱泄漏少,阻带衰减功率大等优点.并且分别用Welch法和全相位法对在有色噪声中混入的双频小信号余弦波进行谱估计,实验证明全相位谱估计性能优于经典谱估计.     

Parameter estimation of autoregressive signals in presence of colored AR(1) noise as a quadratic eigenvalue problem

In this paper, we consider the problem of parameter estimation of autoregressive (AR) signals from observations corrupted with colored AR(1) noise. The proposed method is based on Yule-Walker equations. We express these equations as a quadratic eigenvalue problem and then the parameters of the signal and noise are estimated by solving this eigenvalue problem. We also apply the proposed method to the problem of sinusoidal frequency estimation in colored noise. The performance of the proposed algorithm is evaluated by computer simulation examples.     

色噪声下估计信源数的方法及性能分析

分析了雷达谱估计信号源数的方法.在高频地波雷达中,当各阵元噪声功率非平稳时,提出了一种基于盖氏圆半径和Kullback对称散度的信号源数目估计方法.通过实验证明,该方法能够有效解决在小快拍数、阵元噪声功率不相等情况下信号源数目估计的问题,实现色噪声环境下信源数的估计.     

高斯色噪声下的相干信号 DOA 高分辨率估计*

针对高斯有色噪声下的DOA估计问题,提出一种基于高阶累积量稀疏表示的DOA估计方法。该方法利用四阶累积量矩阵中的第一列生成最小冗余向量,利用扩展阵列的最小冗余导向矢量构造过完备字典。然后利用L1范数作为稀疏约束条件,建立最小冗余向量的稀疏模型进行DOA估计。该方法将求解四阶累积量的次数从M4次降为M2-M+1次。同时又能充分利用四阶累积量的优点,对高斯有色噪声具有良好的抑制能力,并使阵列孔径得到了扩展,估计信号个数能大于阵元数目。仿真实验和理论分析验证了该方法比MUSIC-like和MUSIC算法具有更好的性能,不需要任何处理可以直接应用到相干信号。     

On estimating spectral moments in the presence of colored noise

Let{q^(1) (t)}, the signal, be a complex Gaussian process corrupted by additive Gaussian noise{q^(2) (t) }. Observations onp(t)q(t)andp(t) q^(2) (t)are assumed to be available wherep(t)is a smooth weighting function andq = q^(1) + q^(2). Using the Fourier transform of the samples ofp(t)q(t)andp(t) q^(2) (t), estimators are derived for estimating the mean frequency and spectral width of the unknown power spectrum of the unweighted signal process. The means and variances of these statistics are computed in general, and explicitly for nontrivial practical examples. Asymptotic formulas for the moment estimators as a function of the number of realizations, frequency resolution, signal-to-noise ratio and spectral width, and consistency of the estimators are some of the results that are discussed in detail.     

一种消除非零均值噪声影响的马尔科夫估值方法

本文提出了一种马尔科夫估计方法，用于减小非零均值噪声对测量结果的影响；与求中位数的方法相比，计算量大为减少，文中证明了这种估计方法的无偏性，并给出了应用实例。     

测量噪声背景下微弱正弦信号参数估计的互功率谱方法

本文首次把近代谱估计方法引入到互谱估计中，从理论上证明了互相关函数的Ｙｕｌｅ－Ｗａｌｋｅｒ方程，并在此基础上提出了互谱参数谱估计的矩估计方法和Ｌｅｖｉｎｓｏｎ递推估计方法。该方法可以有效地克服传统的互谱ＦＦＴ算法和互周期图法存在的谱分辩率低，谱估计方差大等缺点。文中还给出了信噪比为－３０ｄＢ的正弦信号参数估计的仿真实例。     

A novel approach for DOA estimation in unknown correlated noise fields

The key of the subspace-based Direction Of Arrival （DOA） estimation lies in the estimation of signal subspace with high quality. In the case of uncorrelated signals while the signals are temporally correlated, a novel approach for the estimation of DOA in unknown correlated noise fields is proposed in this paper. The approach is based on the biorthogonality between a matrix and its Moore-Penrose pseudo inverse, and made no assumption on the spatial covariance matrix of the noise. The approach exploits the structural information of a set of spatio-temporal correlation matrices, and it can give a robust and precise estimation of signal subspace, so a precise estimation of DOA is obtained. Its performances are confirmed by computer simulation results.     

降低雷达接收机中噪声的几种方法

噪声是影响雷达接收机灵敏度的重要因素。本文通过讨论雷达接收机噪声的来源，分析并提出了集中降低雷达接收机内部和外部噪声的几种方法，具有一定的实际意义。     

脉冲噪声环境下的自适应时间延迟估计新方法

基于自适应分数低阶协方差(AFLC)的时间延迟估计方法在脉冲噪声环境下具有良好的韧性,但是算法中参数a和b的取值对于算法的估计精度有一定的影响。针对信号噪声的非平稳特性,该文提出一种动态参数估计方法,并在此基础上提出一种不受约束条件限制的修正的自适应分数低阶协方差(M-AFLC)算法。计算机仿真结果表明,递推参数估计方法在平稳和非平稳噪声环境下都能够很好地工作,M-AFLC算法既保留了AFLC算法的全部优点,又避免了AFLC算法在约束条件不满足时的性能退化。     

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。     

一种快速且准确的噪声估计算法

噪声估计是视频和图像处理最重要的环节之一,实际应用中要求算法能够快速且准确地估计噪声。提出了一种基于块的快速且可靠的噪声估计算法,其中噪声假设为加性零均值高斯白噪声。此算法需要一个N1×N1的窗口,及相应的方差求值运算,避免了排序,复杂度由排序过程的o（n2）或者o（nlog2 n）降为o（n）。此算法中的主要参数会根据图像或者视频信息进行自适应调整,从而提高算法的准确性。通过实验发现,此算法是快速且准确的。     

Adaptive frequency estimation of sinusoidal signals in colored non-Gaussian noises

This paper studies the problem of sinusoidal frequency estimation in colored non-Gaussian ARMA noises. A new adaptive approach is proposed by using the second-and third-order statistics of the measurements. Because of the simultaneous establishment of the signal and noise models, the new approach is applicable for tracking the frequencies at each time instant for stationary and nonstationary signal and/or noise cases. The effectiveness of the new approach is demonstrated by extensive computer simulations. As expected, the approach proposed in this paper outperforms the correlation-based approaches in suppressing the effects of the colored non-Gaussian ARMA noises.     

Detection of the number of two‐dimensional harmonics in additive colored noise

This paper proposes a novel method to detect the number of two‐dimensional (2D) harmonics in additive colored noise based on the enhanced covariance matrix. We define an enhanced covariance matrix using the covariances of the observed signal. We get a special inherent relation between the number of 2D harmonics in additive colored noise and the eigenvalues of the enhanced covariance matrix, which can be used to detect the number of 2D harmonics in additive colored noise by analyzing the eigenvalues of the enhanced covariance matrix. The proposed method has a super resolution and does not need to assume the color and distribution of the additive noise. The effectiveness of the proposed method has been validated by both the theoretical analysis and extensive simulations. Copyright © 2012 John Wiley & Sons, Ltd.     

基于分数低阶矩的非整数时间延迟估计方法

依据信号的噪声特性和分数低阶矩理论,提出一种基于最小平均p范数的非整数时间延迟估计方法(称为LMPFTDE算法)。该算法是对直接估计非整数采样间隔的时间延迟估计算法(ETDGE)的广义化,运用最小分散系数准则,通过使误差的p阶矩最小得到非整数时间延迟估计值。理论分析和计算机仿真结果都表明该方法不仅可以在高斯噪声环境下工作,而且在脉冲噪声下也具有良好的健壮性。     

噪声监测中前景噪声与背景噪声分离方法的研究

为了能够有效的区分前景噪声和背景噪声,本文搭建了一种噪声采集的系统,采用数字噪音计VC824B为噪声采集传感器,单片机作为初级处理端并将噪声数据传输给ARM主控板,再通过通讯板传输给上位机主机。本文提出了在总体噪声的数据中用声强法进行噪声分离。经过实验验证了该方法的可行性,同时为以后提供大规模的噪声采集提供了合理的硬件平台和分析方法。     

Reducing the effects of the noise uncertainty in energy detectors for cognitive radio networks

Because of its ease of implementation and minimum requirements about the primary signals' information, energy detection is broadly considered for signal detection in spectrum sensing algorithms. However, the noise uncertainty phenomenon, caused by the random variations in the noise power, degrades the performance of an energy detector, particularly when the signal‐to‐noise ratio (SNR) is low. In this work, we propose to reduce the negative effects of the noise uncertainty in the performance of an energy detector by dynamically adapting its detection threshold to the noise conditions experienced at each sensing epoch. The noise power is estimated from the received signal samples using an algorithm based on a high‐pass filters bank and median filtering. With our proposal, it is possible to maintain a constant and low false alarm rate in the presence of noise uncertainty, without increasing the probability of misdetection, even in the low SNR regime, and without increasing the number of samples considered for spectrum sensing. Copyright © 2014 John Wiley & Sons, Ltd.     

稳定分布噪声下基于Merid滤波的跳频信号参数估计

常规时频分析方法是处理跳频(FH)信号的有力工具,但在稳定分布噪声环境下无法有效地实现参数估计。该文提出基于Merid滤波的时频分析方法对跳频信号进行参数估计。Merid滤波器可以有效地抑制稳定分布噪声,该文先对观测信号进行Merid滤波,再采用短时傅里叶变换(STFT)进行参数估计。仿真结果表明,在稳定分布噪声环境中,该方法的跳频信号参数估计性能优于基于分数低阶和基于Myriad滤波的两种时频分析方法。