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1.
一种改进的基于高阶累积量的语音盲分离算法   总被引:12,自引:1,他引:12  
盲分离算法被广泛用于语音信号处理领域中,本文给出了一种改进的基于高阶累积量的盲信号分离算法并且被用来进行双路语音增强,本文还提出了最速下降法滤波器抽头系数更新算法。模拟和实际录音的实验表明所提方法的有效性。  相似文献   

2.
基于高阶累积量盲均衡算法的优化设计,提出了一种新的盲均衡算法。该算法通过引入优化思想,利用模拟退火算法来对高阶累积量的SW准则进行优化;利用模拟退火算法的全局收敛性以及快速收敛性,提高了算法的性能。计算机仿真结果表明,该算法具有较好的收敛性能及抗误码性能。  相似文献   

3.
齐赛  杨树元 《信号处理》2005,21(Z1):81-85
基于边界误差性能最优准则(WCPO)的鲁棒性方法是波束形成算法领域内的最新研究热点,本文将该鲁棒性方法与基于高阶累积量的盲波束形成算法相结合,提出了一种新的改进鲁棒性的四阶累积量盲波束形成算法,即WCPO-RCUM算法.该新算法相较于采用传统对角线加载技术的高阶累积量盲波束形成算法RCUM而言,进一步提高了稳健性和输出信干噪比.计算机仿真验证了该算法的可行性和有效性.  相似文献   

4.
本文提出了一种根据系统输出的观测数据对ARMA(AR)系统进行盲识别的新算法。该模型由独立同分布非高斯随机序列驱动,其输出序列中含方差未知的加性高斯噪声。通过求解基于三阶累积量谱的代价函数。该算法以模型阶次递推形式同时辩识ARMA的系统阶次和估计出系统能数。  相似文献   

5.
用遗传算法求解基于高阶累积量的盲均衡问题   总被引:2,自引:1,他引:1  
将遗传算法引入盲均衡技术领域,详细分析了如何用遗传算法求解基于高阶累积量盲均衡问题,并给出了一种求解算法的具体步骤。该算法不用基于领域知识的规则,具有很强的通用性与鲁棒性。通过计算机仿真验证了该算法的有效性。  相似文献   

6.
基于高阶统计判据的红外弱小运动目标检测   总被引:1,自引:0,他引:1       下载免费PDF全文
针对红外预警与跟踪系统中的实时弱小运动目标检测问题,在分析红外灰度图像的非平稳高斯特性的基础上,提出了一种基于高阶统计判据的检测算法。先用一个空域的白化去均值滤波器进行空间背景抑制,为下一步时域高阶统计判据建立一个不相关的高斯背景,根据三阶以上的高阶累积量对于高斯随机过程“盲”的原理,用高阶累积量作二元统计判据检测红外图像背景中的运动弱小目标。算法全面考虑了红外灰度图像和目标在时域与空域方面的特性,大大增强了目标信噪比。通过实际获取的大地背景目标红外数据检测表明,此算法能有效地从复杂背景中检测低信噪比运动小目标,虚警率少,抗噪声干扰能力强。算法易于硬件实现,能够有效地应用于红外搜索与跟踪系统的实时目标检测中。  相似文献   

7.
现有盲均衡算法大多采用先辨识信道、再进行均衡的方法,但信道阶数的估计是一个难点。将高阶归一化累积量用于盲均衡,提出一种基于过采样的六二阶归一化累积量盲均衡算法。该算法不需要辨识信道和估计信道阶数,只对接收信号的六二阶归一化累积量进行优化。仿真结果验证了算法的有效性。  相似文献   

8.
针对MISO通信系统的空时分组码盲识别问题,提出了一种基于高阶累积量的空时分组码盲识别算法。首先,给出了MISO接收信号模型,利用高阶累积量的性质分析得到接收信号的四阶累积量的表达式;然后,利用编码矩阵的特性,证明接收信号在不同时延向量下的四阶累积量呈现非零值,其非零值取决于STBC的类型;最后,采用四阶累积量的实验值与理论值的最小欧式距离盲识别空时分组码的类型。仿真结果表明,即使在低信噪比条件下,所提方法能够较好地识别空时分组码。  相似文献   

9.
突发信号检测是非合作通信中的一项重要工作,是后续处理的前提。针对由高阶累积量作为判决统计量的信号检测方法,分析了高阶累积量法在实际信号检测中存在的问题,提出了相应的改进算法,该算法只用2个符号进行累积量估计,通过在高阶累积量的基础上增加滑动窗,对累积量值进行平滑处理,并通过窗内累积量值的变化自适应调整窗长,减小或消除了因数据过短引起高阶累积量估计值的抖动。仿真结果表明该算法可以提高信号检测性能。  相似文献   

10.
本文提出一种在非高斯ARMA噪声中谐波恢复的高阶累积量方法,该方法首先通过Hiblert变换构造复数观测值,然后使用它的一种特殊的四阶累积量建立噪声过程AR参数,由此对观测值滤波,最后通过SVD-TLS方法估计谐波信号参数,本文方法克服了以往对非高斯噪声分布的非对称性假设,成功地解决了对称分布非高斯有色噪声中的谐波恢复问题,并且适用于于谐波信号存在二次相应耦合情形,仿真实验验证了文中结论。  相似文献   

11.
基于高阶统计的盲均衡算法需要大量的观测数据,当观测数据有限或待道变化较快时,由于模型失配将使均衡性能严重下降,因此短数据、快收敛是近年来盲均衡技术研究的主要方向之一.本文讨论了基于子空间分解的信道识别和盲均衡技术,利用卡尔曼滤波方程,给出了一种快速收敛的盲均衡解调算法,算法只需要较少的数据样本,仿真结果表明该方法是有效的。  相似文献   

12.
Fast identification of autoregressive signals from noisy observations   总被引:1,自引:0,他引:1  
The purpose of this brief is to derive, from the previously developed least-squares (LS) based method, a faster convergent approach to identification of noisy autoregressive (AR) stochastic signals. The feature of the new algorithm is that in its bias correction procedure, it makes use of more autocovariance samples to estimate the variance of the additive corrupting noise which determines the noise-induced bias in the LS estimates of the AR parameters. Since more accurate estimates of this corrupting noise variance can be attained at earlier stages of the iterative process, the proposed algorithm can achieve a faster rate of convergence. Simulation results are included that illustrate the good performances of the proposed algorithm.  相似文献   

13.
A new adaptive algorithm for blind interference rejection and multipath mitigation is studied and applied to antenna array processing in TDMA cellular communication systems. It is shown how the estimation of multiple signals from different sources by means of a multi-sensor receiver can be formulated as a multi-channel deconvolution problem. The proposed method is based on High-Order Statistics (HOS) processing of the baseband vector samples at the antenna array output. The similarity between the cumulant-based solution and the standard multi-variable Least Squares solution is exploited to derive an efficient adaptive algorithm based on the vector lattice architecture. The algorithm is numerically stable, considerably less complex than other existing multi-channel methods using HOS processing and exhibits rapid convergence with respect to blind array processing algorithms using simple gradient-based minimization procedures.  相似文献   

14.
The problem of determining the AR order and parameters of a nonminimum phase ARMA model from observations of the system output is considered. The model is driven by a sequence of random variables which is assumed unobservable. A novel identification algorithm based on the second- and third-order cumulants of the output sequences is introduced. It performs order-recursively by minimising a well defined cost function. Strong convergence and consistency of the algorithm are proved and the weight of the cost function is balanced between the second-order and the third-order cumulants of output sequences. The influence of the weight on the estimation accuracy is also evaluated. Theoretical analyses and numerical simulations show that the proposed algorithm is satisfactory for both order and parameter identification of an AR model which is subordinate to a nonminimum phase ARMA model  相似文献   

15.
本文提出了一种根据系统输出的观测数据对ARMA(AR)系统进行盲识别的新算法。该模型由独立同分布非高斯随机序列驱动,其输出序列中含方差未知的加性高斯噪声。通过求解基于三阶累积量谱的代价函数,该算法以模型阶次递推形式同时辩识ARMA的系统阶次和估计出系统参数。文章给出了该算法一致收敛性的证明,并对两类不同阶次的最小及非最小相位ARMA系统的AR参数及阶次辩识进行了数字仿真,结果令人满意。  相似文献   

16.
This paper presents a new blind XPIC and a new adaptive blind deconvolutional algorithm based on HOS processing, which separates and equalizes the signals in real time. The simulation results demonstrate that the performance of the proposed adaptive blind algorithm, compared with the conventional algorithms, is outstanding with the feature of feasibility, stability and fast convergence rate.  相似文献   

17.
该文分析了在存在噪声干扰的情况下,进行估计快衰信道的方法.在无线通信系统中,快衰信道可以采用AR(Auto-Regressive)模型进行预测,而LS(Least Square)算法和自适应Kalman滤波器可以分别对AR模型的参数和信道的冲激响应进行估计,但是这两利算法对噪声干扰非常敏感.该文提出改进型的RLM算法和Kalman滤波器,并在存在噪声的情况下,使用它们并行对AR参数和信道的冲激响应进行联合估计.仿真结果显示:相比于传统的算法,改进后的算法在联合估计信道时,提高了抵抗大脉冲干扰的能力,加快了待估的参数的收敛速度.  相似文献   

18.
基于最小平均峰度(Least Mean Kurtosis,LMK)准则,该文提出了一种适用于同步直扩码分多址(DS/CDMA)系统多径慢衰落信道中的线性盲自适应多用户检测算法。这种算法基于高阶统计量(High Order Statistics,HOS)特性,算法复杂度相对较低。文中分析了算法在多径信道中无噪声情况下的收敛特性,指出在这种情况下,算法具有全局最小点,此时算法满足解相关条件。仿真结果表明,文中给出的检测器具有较强的多址干扰抑制性能。  相似文献   

19.
Time-varying statistics in linear filtering and linear estimation problems necessitate the use of adaptive or time-varying filters in the solution. With the rapid availability of vast and inexpensive computation power, models which are non-Gaussian even nonstationary are being investigated at increasing intensity. Statistical tools used in such investigations usually involve higher order statistics (HOS). The classical instrumental variable (IV) principle has been widely used to develop adaptive algorithms for the estimation of ARMA processes. Despite, the great number of IV methods developed in the literature, the cumulant-based procedures for pure autoregressive (AR) processes are almost nonexistent, except lattice versions of IV algorithms. This paper deals with the derivation and the properties of fast transversal algorithms. Hence, by establishing a relationship between classical (IV) methods and cumulant-based AR estimation problems, new fast adaptive algorithms, (fast transversal recursive instrumental variable-FTRIV) and (generalized least mean squares-GLMS), are proposed for the estimation of AR processes. The algorithms are seen to have better performance in terms of convergence speed and misadjustment even in low SNR. The extra computational complexity is negligible. The performance of the algorithms, as well as some illustrative tracking comparisons with the existing adaptive ones in the literature, are verified via simulations. The conditions of convergence are investigated for the GLMS  相似文献   

20.
Volterra filters (VFs) and higher order statistics (HOS) are important tools for nonlinear analysis, processing, and modeling. Despite their highly desirable properties, the transfer of VFs and HOS to real-world signal processing problems has been hindered by the requirement of very large data records needed to obtain reliable estimates. The identification of VFs and the estimation of HOS both fall into the category of ill-posed estimation problems. We develop penalized least squares (PLS) estimation methods for VFs and HOS. It is shown that PLS is a very effective way to incorporate prior information of the problem at hand without directly constraining the estimation procedure. Hence, PLS produces much more reliable estimates. The main contributions of this paper are the development of appropriate penalizing functionals and cross-validation procedures for PLS based VF identification and HOS estimation  相似文献   

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