四阶累积量稀疏表示的DOA估计方法

针对现有稀疏重构DOA估计算法不能抑制噪声项以及在高斯色噪声背景下不再适用的问题,本文提出了基于四阶累积量稀疏重构的DOA估计方法。首先,利用接收数据的四阶累积量构建了稀疏表示模型,该模型抑制了噪声项;其次对四阶累计量矩阵进行奇异值分解,化简了稀疏表示模型,通过奇异值分解,不仅减小了数据规模,而且进一步抑制了噪声。对于稀疏表示模型的求解,先利用信号子空间与噪声子空间的正交特性选取权值矢量,然后利用加权l1范数法对模型求解实现DOA估计。理论分析和仿真实验表明本文算法在高斯白噪声和色噪声背景下均适用;能够处理非相干和相干信号,且在低信噪比条件下,对相干信号有更高的估计精度;较之同类的稀疏重构算法,本文算法具有较低的算法复杂度和更高的角度分辨力。      

信道估计中基于变换域的降噪处理技术

本文讨论了信道估计中的降噪处理技术，根据信道中加性高斯噪声的特点，提出在变换城中抑制噪声能量的方法。大大提高了估计精度。文中分析了两种降噪算法，并从理论上分析了算法一的处理增益。仿真表明．经过降噪算法二处理后信道估计精度得到了大犬的提高。其估计误差时系统误码性能的影响已经极小，单纯靠提高信道估计精度已很难进一步改善系统的误码性能。     

非均匀噪声环境下宽带近场源定位的两种改进方法

研究了宽带近场信号源基于最大似然方法和相关信号子空间方法在非均匀噪声下的被动定位算法，并进行了比较。这两种算法均可在传感器任意分布的情况下有效地进行信号源定位。最大似然法采用了迭代的方法来估计噪声的协方差矩。而信号子空间法给出了聚焦阵构造的新方法。仿真试验证明了方法的有效性和稳健性。     

一种基于自适应噪声估计的宽带语音增强算法

为解决传统算法对噪声适应性较差,残留音乐噪声较强的问题,本文提出了一种基于自适应噪声估计的宽带语音增强算法。该算法可应用于宽带语音编码器,以提升在噪声环境下的编码质量。本文所提算法利用谱熵对噪声类型进行有效的判别,将背景噪声分为白噪声和有色噪声两类,并根据噪声特性选择适当的噪声估计方法。在白噪声背景下,选择一种谱平滑的方法;在有色噪声背景下,则选择经典的最小值控制递归平均算法。在此基础上结合经典的统计模型方法,构建一种具有较强噪声鲁棒性的宽带语音增强算法。在ITU-T G.160标准下对算法进行性能测试,测试结果表明,在不同强度的背景噪声环境下,增强语音的信噪比提高都较为明显。同时,在低信噪比情况下,该算法有效的抑制了严重影响听觉质量的音乐噪声现象。      

Subspace-based direction-of-arrival estimation using nonparametricstatistics

The problem of subspace estimation using multivariate nonparametric statistics is addressed. We introduce new high-resolution direction-of-arrival (DOA) estimation methods that have almost optimal performance in nominal conditions and are robust in the face of heavy-tailed noise. The extensions of the techniques for the case of coherent sources are considered as well. The proposed techniques are based on spatial sign and rank concepts. We show that spatial sign and rank covariance matrices can be used to obtain convergent estimates of the signal and noise subspaces. In the proofs, the noise is assumed to be spherically symmetric. Moreover, we illustrate how the number of signals may be determined using the proposed covariance matrix estimates and a robust estimator of variance. The performance of the algorithms is studied using simulations in a variety of noise conditions including noise that is not spherically symmetric. The results show that the algorithms perform near optimally in the case of Gaussian noise and highly reliably if the noise is non-Gaussian     

冲击噪声下基于子空间拟合的DOA估计新算法

研究了在对称α稳定分布(Symmetric α-stable,SαS)冲击噪声背景下的基于子空间拟合的多目标DOA估计算法.由于SαS冲击噪声造成传统DOA算法性能下降,提出了基于分数低阶矩统计量的FLOM-SSF/NSF算法和Screened Ratio原理的SR-SSF/NSF算法.计算机仿真表明两种算法都能有效地改善SαS型冲击噪声对DOA估计的影响,有更好的稳健性,其中SR-SSF/NSF算法性能略好于FLOM-SSF/NSF算法.     

A new correntropy based TDE method under <Emphasis Type="Italic">α</Emphasis>-stable distribution noise environment

This paper presents a robust time delay estimation algorithm for the α-stable noise based on correntropy. Many time delay estimation algorithms derived for impulsive stable noise are based on the theory of Fractional Lower Order Statistics (FLOS). Unlike previously introduced FLOS-type algorithms, the new algorithm is proposed to estimate the time delay by maximizing the generalized correlation function of two observed signals needing neither prior information nor estimation of the numerical value of the stable noise’s characteristic exponent. An interval for kernel selection is found for a wide range of characteristic exponent values of α-stable distribution. Simulations show the proposed algorithm offers superior performance over the existing covariation time delay estimation, least mean p-norm time delay estimation and achieves slightly improved performance than fractional lower order covariance time delay estimation at lower signal to noise ratio when the noise is highly impulsive     

Two Novel Channel Estimation for OFDM Systems by Time-Domain Cluster Discriminant Analysis Based on Parametric Channel Modeling

In this paper, we describe two novel channel estimation technologies for orthogonal frequency-division multiplexing mobile communication systems using cluster discriminant analysis for sparse multipath channels. The least-squares estimator has a merit of low complexity and simple structure; however one of its drawbacks is that it does not take into consideration the effect of noise. Conventional DFT-based channel estimator improved its performance by suppressing time domain noise, but it does not completely suppress the noise. In order to overcome this disadvantage, we propose two novel channel estimation algorithms for Orthogonal frequency division multiplexing systems based on cluster analysis and discriminant analysis. The cluster analysis can be used to cluster residual noise. Discriminant analysis can distinguish the noise and channel taps in time domain. Computer simulation demonstrates the performance of the proposed algorithms in terms of bit error rate and mean square error performance.     

Parameter Estimation Based on Fractional Power Spectrum Density in Bistatic MIMO Radar System Under Impulsive Noise Environment

This paper takes an Alpha-stable distribution as the noise model to solve the parameter estimation problem of bistatic multiple-input multiple-output (MIMO) radar system in the impulsive noise environment. For a moving target, its echo often contains a time-varying Doppler frequency. Furthermore, the echo signal may be corrupted by a non-Gaussian noise. It causes the conventional algorithms and signal models degenerating severely in this case. Thus, this paper proposes a new signal model and a novel method for parameter estimation in bistatic MIMO radar system in the impulsive noise environment. It combines the fractional lower-order statistics (FLOS) and fractional power spectrum density (FPSD), for suppressing the impulse noise and estimating parameters of the target in fractional Fourier transform domain. Firstly, a new signal array model is constructed based on the \(\alpha \)-stable distribution model. Secondly, Doppler parameters are jointly estimated by peak searching of the FLOS–FPSD. Furthermore, two modified algorithms are proposed for the estimation of direction-of-departure and direction-of-arrival (DOA), including the fractional power spectrum density based on MUSIC algorithm (FLOS–FPSD–MUSIC) and the fractional lower-order ambiguity function based on ESPRIT algorithm (FLOS–FPSD–ESPRIT). Simulation results are presented to verity the effectiveness of the proposed method.     

复杂环境下基于噪声匹配的最大多普勒频移估计

本文针对城市复杂信道环境下的最大多普勒频移估计需求,根据莱斯衰落信道中电平通过率(LCR)算法的理论推导,提出了一种基于噪声匹配的最大多普勒频移估计算法.所提算法通过对接收信号进行低通滤波处理,实现干扰噪声与多普勒检测器之间的匹配,从而有效提高最大多普勒频移的估计性能.而且基于莱斯衰落信道下最佳滤波比值的分析和推导,得...     

基于QR分解的MIMO信道盲辨识和盲均衡方法

针对SIMO信道的经典盲估计方法,如子空间法(SS)等,都是基于接收端样本自相关阵的特征值分解(EVD)或奇异值分解(SVD)来实现信道估计的,而基于QR分解的SIMO信道盲辨识方法是最近提出的一种性能优良的新算法.本文将该算法推广为MIMO信道盲辨识算法,并且证明了在一定的假设下,即使各路源信号为空间相关且其统计特性未知时,该算法仍然保持有效.实验结果表明这种MIMO辨识算法具有收敛速度快、计算量小、无须对噪声做额外的处理、对噪声不敏感等优点.我们还将这种算法与经典的MIMO辨识算法进行了性能比较.     

Recursive Extended Least Squares Parameter Estimation for Wiener Nonlinear Systems with Moving Average Noises

Many control algorithms are based on the mathematical models of dynamic systems. System identification is used to determine the structures and parameters of dynamic systems. Some identification algorithms (e.g., the least squares algorithm) can be applied to estimate the parameters of linear regressive systems or linear-parameter systems with white noise disturbances. This paper derives two recursive extended least squares parameter estimation algorithms for Wiener nonlinear systems with moving average noises based on over-parameterization models. The simulation results indicate that the proposed algorithms are effective.     

The resolution threshold of a direction-finding algorithm fordiversely polarized arrays

Subspace-based algorithms for narrowband direction-of-arrival (DOA) estimation require detailed knowledge of the array response (the array manifold) and assume that the noise covariance matrix is known up to a scaling factor. In practice, these quantities are not known precisely. Resolution and estimation accuracy can degrade significantly when the array response or the noise covariance deviate from their nominal values. We examine the resolution threshold of a recently proposed subspace-based algorithm for direction finding with diversely polarized arrays. We study finite sample effects, and the effects of modeling errors (errors in the array manifold or the noise covariance), on the resolution threshold. A comparison is made between the resolution thresholds of the MUSIC algorithm (for uniformly polarized arrays) and the proposed algorithm (for diversely polarized arrays)     

Integrating recursive minimum tracking and codebook-based noise estimation for improved reduction of non-stationary noise

Conventional single-channel noise reduction algorithms typically have problems with non-stationary noise. Popular algorithms such as minimum statistics or voice-activity-detector-based methods rely on the assumption that the noise spectral characteristics change very slowly over time. Codebook-based approaches try to overcome this problem by incorporating a priori knowledge about speech and different noise types. These approaches perform a joint estimation of the speech and noise spectra on a frame-by-frame basis. The frames are typically 20-40 ms long so that fast fluctuations of the signal characteristics can be tracked instantaneously. However, these methods require a pitch estimator to prevent speech distortion as well as residual noise in voiced speech frames. In addition, they are not very robust against model mismatch. In this paper, we propose an integrated noise estimation algorithm that combines the ability of codebook-based algorithms to track non-stationary noise with the robustness of a recursive minimum-tracking-based noise estimation algorithm. An objective and subjective evaluation is provided. Results confirm the superiority of the proposed algorithm in non-stationary noise scenarios compared to state-of-the-art algorithms.     

基于稳定分布噪声稀疏性及最优匹配的跳频信号参数估计

目前基于压缩感知的跳频信号参数估计方法大多是在高斯背景噪声下进行的研究,而在非高斯稳定分布脉冲噪声环境下,已有基于高斯噪声数学模型设计的算法性能下降。针对上述问题,该文分析了稳定分布噪声的大幅值脉冲满足近似稀疏性条件,利用跳频信号与噪声之间的时域特征差异将信噪分离,实现噪声抑制。并在压缩感知框架下,建立与跳频信号特点相匹配的3参数字典,采用最优匹配(Optimal Match, OM)方法对跳频信号自适应分解,获取匹配原子,基于这些时频原子包含的信息估计跳频信号的参数。仿真验证表明,在稳定分布噪声中,与常规的跳频信号估计方法相比,该文提出的先利用噪声稀疏性去噪,再采用最优匹配提取跳频信号参数的方法(Sparsity-OM, SOM),能够较好地抑制脉冲噪声,获得准确的参数信息,具有良好的鲁棒特性。     

Parameter estimation based on fractional power spectrum under alpha-stable distribution noise environment in wideband bistatic MIMO radar system

This paper takes the alpha-stable distribution as the noise model and works on the parameter estimation problem of wideband bistatic Multiple-Input Multiple-Output (MIMO) radar system in the impulsive noise environment. In many applications, it is not appropriate to approximate the wideband signal by the narrowband model. Furthermore, the echo signal may be corrupted by the non-Gaussian noise. The conventional algorithms degenerate severely in the impulsive noise environment. Thus, this paper proposes a new wideband signal model and a novel method in wideband bistatic MIMO radar system. It combines the fractional lower order statistics and fractional power spectrum, for suppressing the impulse noise and estimating parameters of the target. Firstly, a new signal array model is proposed under the alpha-stable distribution noise model. Secondly, Doppler stretch and time delay are jointly estimated by peak searching of the FLOS-FPSD. Furthermore, two modified algorithms are proposed for the estimation of the direction-of-departure and direction-of-arrival, including the fractional power spectrum density based on MUSIC algorithm (FLOS-FPSD-MUSIC) and the fractional lower-order ambiguity function based on ESPRIT algorithm (FLOS-FPSD-ESPRIT). Simulation results are presented to verity the effectiveness of the proposed method.     

一种基于噪声估计的语音激活检测算法

针对当前语音激活检测算法在低信噪比和复杂噪声模型的环境下性能损失的问题,提出了一种基于噪声估计的语音激活检测算法,通过对背景噪声进行自适应估计,得到准确的信噪比门限,同时利用估计背景噪声对短时谱进行白化处理,从而使得谱熵判决准则得以适用于复杂噪声模型的环境。实验证明,算法在低信噪比和复杂噪声模型下性能优于G.729B和AMR中的语音激活检测算法。     

脉冲噪声下基于相关熵的相干分布源DOA估计新方法

针对复杂电磁环境下被动无线监测定位问题,该文提出广义相关熵的概念,推导了广义相关熵的性质,用以抑制阵列输出信号中的脉冲噪声。为了实现脉冲噪声环境下相干分布源中心DOA和扩散角的联合估计,提出基于广义相关熵的DOA估计新方法,并证明了该方法的有界性。为进一步提升算法的鲁棒性,推导了一种仅依赖阵列输出信号的自适应核函数。仿真结果表明,该算法能够实现脉冲噪声环境下相干分布源参数的联合估计,相比已有算法,具有更高的估计精度和鲁棒性。     

基于主成分分析与深度神经网络的快速噪声水平估计算法

鉴于从噪声图像分解获得的原生图块集合的协方差矩阵前若干个特征值（按照升序排序）与图像噪声水平值具有强相关性,提出了一种基于主成分分析和深度神经网络的快速噪声水平估计算法.该算法首先选用原生图块集合协方差矩阵前若干个特征值构成刻画图像噪声水平高低的特征矢量,然后在大量有代表性且已标定噪声水平值的噪声图像集合上利用深度神经网络训练预测模型以实现将特征矢量直接映射为噪声水平值,最后为获得更高的预测准确性,采用粗精预测模型相结合的两步预测方式实现.实验表明：文中算法在各个噪声级别上都具有稳定的预测准确性,且执行效率非常高,作为降噪算法的前置预处理模块具有更好的综合优势.     

基于平行嵌套阵互协方差的二维波达角联合估计算法

论文提出基于平行嵌套阵互协方差的2维(Two Dimensional, 2D)波达角(Direction Of Arrival, DOA)联合估计算法。算法基于两个互相平行的嵌套阵的互协方差生成较长虚拟阵列,同时将2维DOA估计问题降维为1维 DOA估计问题。在构造协方差矩阵时,利用方向矩阵范德蒙特性增加虚拟快拍数,保证了孔径的最小损失。最后算法基于酉旋转不变技术(Estimation of Signal Parameters via Rotational Invariance Technique, ESPRIT)和总体最小二乘(Total Least Squares, TLS)方法进一步降低噪声影响,并获得了自动配对的2维DOA估计。相比传统平行阵下的DOA估计算法,该算法拥有更好的DOA估计性能,能辨识更多的空间信源,对空间色噪声有更强的鲁棒性。仿真结果验证了算法的有效性。