冲激噪声环境下基于最大相关熵准则的韧性子空间跟踪新算法

本文采用最大相关熵准则(MCC)对投影近似子空间跟踪(PAST)算法中基于最小平方误差(MSE)准则的目标函数进行修正,推导出适用于冲激噪声环境的韧性投影近似子空间跟踪新算法(MCC_ PAST算法).对两种冲激噪声模型包括,稳定分布噪声模型和混合高斯噪声模型环境下的时变波达方向估计问题的实验仿真表明,与基于韧性M估计的PAST改进算法(RLM_ PAST算法)相比,MCC_ PAST算法可以自适应地调整核长,对于阵列接收数据的变化体现出更好的适应性.     

一种新的基于改进PASTd的中频信号盲信噪比估计算法

该文提出一种加性高斯白噪声信道下基于改进的紧缩投影近似子空间跟踪(PASTd)的中频信号盲信噪比估计算法。将Gram-Schmidt正交化过程引入到PASTd中,使计算得到的特征向量相互正交,从而保证算法具有更好的收敛性能。对MPSK(M=2,4,8)信号和MQAM(M=16,64,128,256)信号进行了大量计算机仿真,结果表明该算法性能稳定,并且当信噪比变化范围为5dB到25dB时,所得到的估计偏差小于1dB,估计标准差在0.3以内。与基于特征值分解的算法相比,能够在得到精确估计结果的同时,大大减小运算复杂度。     

基于导频信号的OFDM通信系统信道估计与跟踪

讨论了在正交频分复用(OFDM)通信系统中,基于导频信号,采用投影逼近子空间(PAST)算法实现信道的跟踪估计,在快速变化的信道中,信道阶数自动跟踪,与传统的信道估计方法相比,信道估计精度提高,均方误差减小.通过计算机模拟仿真,验证了信道估计的跟踪性能得到了改善.     

基于投影近似子空间跟踪技术的自聚焦算法

基于特征向量法的自聚焦算法具有比相位梯度自聚焦(Phase Gradient Autofocus,简称PGA)算法更好的算法性能,但该算法必须对协方差矩阵进行特征分解,所以运算量大.利用投影近似子空间跟踪(Projection Approxima-tion Subspace Tracking,简称PAST)技术的自聚焦算法可以解决上述问题.通过实际数据处理结果对比,证明基于PAST技术的自聚焦算法是一种可满足实时处理要求的有效自聚焦方法.     

一种对称α稳定分布噪声环境下DOA估计新算法

脉冲噪声环境下波达方向(DOA)估计是阵列信号处理领域一个新兴研究方向。针对α稳定分布噪声环境下经典MUSIC算法性能退化的问题,提出了一种新的基于非线性压缩核函数(NCCF)的DOA估计算法。该算法利用基于NCCF的有界矩阵代替了MUSIC的协方差矩阵,通过对有界矩阵进行特征分解确定信号子空间和噪声子空间,借用MUSIC谱估计公式进行谱峰搜索,得到DOA的估计值。仿真结果表明,NCCF-MUSIC算法运算复杂度较低,相比于基于分数低阶统计量(FLOS)的MUSIC方法和基于广义类相关熵(GCAS)的MUSIC算法,该方法具有更好的准确度和稳定性。     

OFDM系统中的信道自适应估计与跟踪算法比较

传统的QR分解和投影逼近子空间（PAST）分解算法,可用于矩阵的奇异值分解和秩数的估计。讨论了在正交频分复用（OFDM）通信系统中基于这两种分解方法对导引信号的子空间进行分解,实现信道矩阵的自适应跟踪估计。采用这种算法,降低了矩阵运算的维数,使每个符号期间信道估计的运算量减少,信道估计的均方误差减小,接收机的误码性能得到改善,同时利用计算机模拟,对两种算法运用到信道估计中的跟踪性能进行了比较。     

基于滑动窗与韧性函数的递归最小p范数滤波方法

稳定分布可以更好地描述实际应用中所遇到的具有显著脉冲特性的随机信号和噪声。与其它统计模型不同, 稳定分布没有统一闭式的概率密度函数,其二阶及二阶以上统计量均不存在。针对系统中存在独立SS噪声与高斯噪声,该文基于SSG分布模型,提出了一种混合噪声环境下基于滑动窗与韧性函数自适应广义递归最小p范数滤波算法,并对算法进行了分析。计算机模拟和分析表明,这种算法是一种在SSG分布背景噪声条件下具有良好鲁棒性的方法。     

基于加权噪声的递归平滑噪声功率谱估计

提出了一种噪声功率谱估计算法,该算法对加权后的带噪语音进行递归平滑,可以持续更新噪声并可应用于非平稳噪声环境中。为了避免在强语音后的弱语音区域出现噪声过估计,本文提出了用于计算加权函数的投影平滑算法。本文噪声估计算法可以快速跟踪噪声的变化并且没有过估计。实验结果表明,本文噪声估计算法应用于一个语音增强系统时,取得了较小的噪声分段估计误差及较好的感知语音质量评价(PESQ)得分。     

一种改进的最小统计量控制递归平均噪声估计算法

本文提出了一种改进的最小统计量控制递归平均噪声估计算法.算法采用递归平均进行噪声估计,其递归平均的平滑因子受语音存在概率控制,而语音存在概率的计算采用了两次平滑和最小统计量跟踪.与I.Cohen提出的IMCRA算法相比,本文采用了一种快速有效的最小统计量跟踪算法.仿真结果表明在非平稳噪声条件下,算法具有较好的噪声跟踪能力和较小的噪声估计误差,可以有效地提高语音增强系统的性能.     

一种Alpha稳定分布噪声下的体积目标长度估计方法

为了解决Alpha稳定分布噪声环境下运动舰船目标的长度估计问题,该文借鉴非线性变换抑制脉冲噪声以及多普勒目标运动特性估计思想,提出基于广义时频分析(G-TFA)和最小二乘估计的运动目标长度估计方法。该方法首先利用G-TFA获取Alpha稳定分布噪声环境下运动目标的多普勒频率,然后利用最小二乘方法估计出目标航速和不同位置的横正时刻,最后利用上述估计结果计算目标长度。以广义Winger-Ville分布(G-WVD)为例,从理论上推导了G-TFA在Alpha稳定分布噪声环境下具有提取目标多普勒特征的能力,并通过仿真实验验证了该算法在中低混合信噪比下的稳健性。与现有算法相比,该文所提算法不需要估计噪声特征指数,算法性能优于基于传统时频分析的估计方法。     

A robust past algorithm for subspace tracking in impulsive noise

The PAST algorithm is an effective and low complexity method for adaptive subspace tracking. However, due to the use of the recursive least squares (RLS) algorithm in estimating the conventional correlation matrix, like other RLS algorithms, it is very sensitive to impulsive noise and the performance can be degraded substantially. To overcome this problem, a new robust correlation matrix estimate, based on robust statistics concept, is proposed in this paper. It is derived from the maximum-likelihood (ML) estimate of a multivariate Gaussian process in contaminated Gaussian noise (CG) similar to the M-estimates in robust statistics. This new estimator is incorporated into the PAST algorithm for robust subspace tracking in impulsive noise. Furthermore, a new restoring mechanism is proposed to combat the hostile effect of long burst of impulses, which sporadically occur in communications systems. The convergence of this new algorithm is analyzed by extending a previous ordinary differential equation (ODE)-based method for PAST. Both theoretical and simulation results show that the proposed algorithm offers improved robustness against impulsive noise over the PAST algorithm. The performance of the new algorithm in nominal Gaussian noise is very close to that of the PAST algorithm.     

A robust adaptive weighted constant modulus algorithm for blind equalization of wireless communications systems under impulsive noise environment

A robust adaptive weighted constant modulus algorithm is proposed for blind equalization of wireless communication systems under impulsive noise environment. The influence of the impulsive noise is analyzed based on numerical analysis method. Then an adaptive weighted constant modulus algorithm is constructed to adaptively suppress impulsive noise. Theoretical analysis is provided to illustrate that the proposed algorithm has a robust equalization performance since the impulsive noise is adaptively suppressed. Moreover, the proposed algorithm has stable and quick convergence due to avoidance of large misadjuntment and adoption of large step size. Simulation results are presented to show the robust equalization performance and the fast convergence speed of the proposed algorithm under both impulsive noise and Gaussian noise environments.     

脉冲噪声环境下基于宽带模糊函数的双基地MIMO雷达目标参数估计新方法

以Alpha稳定分布作为噪声模型,研究了脉冲噪声环境下宽带双基地MIMO雷达系统中参数估计问题.针对在脉冲噪声环境中,基于传统的信号模型和算法效果显著退化的问题,本文提出了基于分数低阶统计量的宽带模糊函数算法.首先根据分数低阶宽带模糊函数的峰值点实现对多普勒频率尺度因子和时延的联合估计.接下来基于分数低阶宽带模糊函数构造两个子阵.通过采用改进的MUSIC算法和ESPRIT算法实现了收发角的联合估计.仿真实验表明本文算法具有很好的性能.     

Novel method based on fractional lower-order correntropy-analogous statistics for parameter jointly estimation in bistatic MIMO radar

According to the performance degradation problem of parameter estimation algorithm in the Alpha stable dis-tribution noise, inspired by the concept of correntropy, a new class of statistics, namely, the fractional lower-order cor-rentropy-analogous statistics (FCAS) was proposed. By employing the fractional lower-order correntropy-analogous sta-tistics based cost function in parallel factor (PARAFAC), the FCAS-PARAFAC algorithm was deduced which can be utilized for the parallel factor under impulsive noise environments. The FCAS-PARAFAC algorithm was applied to pa-rameter estimation in bistatic MIMO radar under impulsive noise environment. The proposed method can suppress the impulse noise interference and has better estimation performance. Furthermore, the estimated parameters are automati-cally paired without the additional pairing method. Simulation results are presented to verify the effectiveness of the pro-posed method.     

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     

Robust Recursive Beamforming in the Presence of Impulsive Noise and Steering Vector Mismatch

This paper proposes a new method for robust beamforming in the presence of impulsive noise as well as steering vector mismatch. In our proposed method, the idea of M-estimation is firstly incorporated into the traditional orthonormal PAST (OPAST) algorithm for subspace tracking to combat the hostile effect of impulsive noise. Taking advantage of the subspace principle, we show that the steering vector mismatch can be recursively and robustly estimated in closed form. Then, by making use of the estimated steering vector, the problem of robust beamforming in the presence of impulsive noise is formulated. The solution of this problem is analytically derived and the resultant robust beamformer is shown to have a similar form to the Capon beamformer, whereas the array covariance matrix and the steering vector are robustly estimated. Different from conventional methods, the impulsive noise and the steering vector mismatch are simultaneously handled by extending the traditional OPAST algorithm, and hence the proposed method has low complexity and it is feasible to nonstationary scenarios with moving sources. Simulation results demonstrate the validity and superiority of the proposed method over conventional methods in impulsive noise environment with steering vector mismatch.     

广义复相关熵与相干分布式非圆信号DOA估计

针对相干分布式非圆信号参数估计算法在脉冲噪声环境下性能退化的问题,本文提出了广义复相关熵的概念,并给出了基于广义复相关熵的相干分布式非圆信号DOA（Direction of Arrival）估计方法。该算法首先由分布式信源模型获得入射信号的阵列输出信号,利用信号的非圆特性得到扩展阵列输出信号,再通过扩展阵列输出信号的广义复相关熵矩阵获取信号子空间,避开了传统二阶统计量算法在脉冲噪声下不适应的问题,最后由信号子空间旋转不变特性得到信号的中心波达方向角度。仿真实验结果表明,在Alpha稳定分布噪声条件下,与传统算法相比,本文所提算法具有更好的性能。      

Stable adaptive channel estimation method under impulsive noise environments

Channel estimation is one of the key technologies for ensuring reliable wireless communications under impulsive noise environments. This paper studies robust adaptive channel estimation methods for mitigating harmful impulsive noises, which are described as alpha‐stable (α ‐stable) distribution models. Traditional adaptive channel estimation using the second‐order statistics based least mean square (SOS‐LMS) algorithm does not perform well under α ‐stable noise environments, even though it was considered one of attractive approaches for estimating channels in the case of Gaussian noises. Unlike the traditional SOS‐LMS algorithm, in this research, we propose a stable sign‐function‐based LMS algorithm, which can mitigate the impulsive noises. Specifically, we first construct the cost function with minimum ℓ ₁‐norm error criterion and then derive the updating equation of the proposed algorithm. Compared with the traditional SOS‐LMS, the effectiveness of the proposed algorithm is validated via Monte Carlo simulations in various α ‐stable noise scenarios. Copyright © 2015 John Wiley & Sons, Ltd.     

对称Alpha稳定分布噪声下单天线干扰消除算法

蜂窝移动通信系统性能受限于码间干扰、同频干扰和脉冲噪声等因素。本文提出一种基于粒子滤波的单天线干扰消除算法。首先,对脉冲噪声采用Alpha稳定分布进行建模,并对该模型进行高斯近似,递推得到多个未知信道参数的联合后验概率。其次,提出基于延迟粒子滤波的同信道传输码元最大后验估计方法。理论推导和仿真实验结果表明本文算法能够消除码间干扰和同频干扰对码元检测的影响,与其他干扰消除算法相比,特别是在强脉冲噪声和未知信道参数情况下,具有一定的优势。