共查询到18条相似文献,搜索用时 93 毫秒
1.
2.
为了获取单通道接收信号的信源数目,针对普通信源数估计方法不能直接用于单通道接收信号的问题,提出了基于经验模态分解(empirical mode decomposition, EMD)的信源数估计方法。将单通道信号通过EMD处理,得到多个固有模态函数(intrinsic-mode function, IMF),据此构造数据协方差矩阵。对所构造的协方差矩阵进行特征值分解,采用基于信息论的AIC和MDL准则估计信源数。为进一步提高算法估计性能,引入对角加载技术对矩阵特征值进行平滑处理。仿真实验结果表面,本文提出的方法能够适用于单通道信源数估计,对角加载技术能够显著提高算法检测性能。 相似文献
3.
经典的特征值类信源数估计算法在低信噪比、少快拍数条件下的估计性能急剧下降,针对该问题,提出了一种新的信源数估计算法。该算法利用采样协方差矩阵的特征向量对信噪比不敏感的特性来构造判决变量,根据改进的预测描述长度(PDL)准则来实现对信源数的有效估计,理论分析和仿真实验证明了所提算法的有效性。 相似文献
4.
考虑到色噪声或低快条件下噪声特征值发散,导致基于特征分解的信源数估计方法得到的信号判据值和噪声判据值区分不明显,提出了一种基于加权特征投影的信源数估计方法;首先,为了使该方法可适用于低信噪比条件,对阵列接收数据的协方差矩阵进行降噪处理,并利用降噪后协方差矩阵所有特征值和特征向量构造了一个用来区分信号和噪声的加权空间矩阵;然后,将降噪后的协方差矩阵在该加权空间矩阵上投影,从而增大了信号判据值与噪声判据值的差异;最后,结合幂函数的缩放性构建了判决函数,进而实现信源数估计;通过理论分析和实验验证,该方法不仅适用于白噪声和色噪声条件,而且在低快拍和低信噪比条件下优势明显,在快拍数为10,信噪比分别为0 dB的白噪声和6 dB的色噪声条件下,该方法的成功检测概率均达到90%以上,同时该算法在信源数较多时效果鲁棒. 相似文献
5.
针对均匀线列阵,在宽带混合信号(不相关和相干信号共存)情形下,提出了一种DOA快速估计新算法。利用阵列协方差矩阵的Hermitian性,通过酉变换将各频点的复数据矩阵映射为实矩阵,通过实值化的TOFS法先直接估计出宽带非相关信号的DOA;然后利用空间差分技术,在各个频点上得到只含相干信号的数据协方差矩阵;通过Toeplitz矩阵重构,在不降低阵列孔径的条件下,可实现相干信号的解相干,再利用实值TOFS法可得到相干信号的DOA。由于算法是并行分别对不相关和相干信号进行DOA估计,在信源过载(信号数大于阵元数)的情形下,算法依然有效,同时由于实值化,算法的计算复杂度较小。仿真结果验证了算法的有效性。 相似文献
6.
提出一种多径平坦衰落信道下的盲信噪比估计方法.该算法首先利用数字通信信号的循环平稳统计特性构造接收信号的循环自相关矩阵,然后对该矩阵进行奇异值分解,由分解出的特征值信号子空间和噪声子空间,最后通过利用AIC信息准则分别估计信号子空间和噪声子空间的维数并最终估计出信道的平均信噪比.以MPSK信号为例进行了计算机仿真,结果表明了算法的有效性. 相似文献
7.
针对稀疏信号盲源分离势函数法需要过多参数,以及聚类算法需要知道源信号个数的缺陷,采用基于拉普拉斯模型的势函数法估计源信号数目和混合矩阵。将混合信号重新聚类,对每一类信号的协方差矩阵进行奇异值分解,混合矩阵得到更精确的估计,进而源信号也得到更精确的估计。通过计算机仿真,表明了该算法的优越性。 相似文献
8.
利用阵列信号处理时域与空域等效的关系,以平面阵为基础,采用阵列协方差矩阵的奇异值分解和广义特征值分解估计接收信号的频率,通过分析阵列模型,提出一种抗原和抗体的亲和力函数;利用量子免疫进化的特性,估计出信号的俯仰角和方位角,从而完成阵列信号的多维参数估计,改善了多维参数估计的抗噪性能、数值稳定性和运行时间。通过计算机仿真,证明了该算法的有效性。 相似文献
9.
10.
11.
In this paper we present a new patch-based empirical Bayesian video denoising algorithm. The method builds a Bayesian model for each group of similar space-time patches. These patches are not motion-compensated, and therefore avoid the risk of inaccuracies caused by motion estimation errors. The high dimensionality of spatiotemporal patches together with a limited number of available samples poses challenges when estimating the statistics needed for an empirical Bayesian method. We therefore assume that groups of similar patches have a low intrinsic dimensionality, leading to a spiked covariance model. Based on theoretical results about the estimation of spiked covariance matrices, we propose estimators of the eigenvalues of the a priori covariance in high-dimensional spaces as simple corrections of the eigenvalues of the sample covariance matrix. We demonstrate empirically that these estimators lead to better empirical Wiener filters. A comparison on classic benchmark videos demonstrates improved visual quality and an increased PSNR with respect to state-of-the-art video denoising methods. 相似文献
12.
针对传统认知无线电网络(CRN)的频谱感知策略没有考虑噪声不确定性问题,提出一种基于噪声功率估计自适应阈值和OR-决策规则的频谱感知策略。首先,将各接收器数据构建成一个数据矩阵,并计算矩阵的协方差矩阵。然后,计算协方差矩阵的特征值,并根据特征值的均值来获得噪声的最大似然估计。接着,根据估计的噪声和能量信号的检验统计量来确定决策阈值。最后,各节点根据决策阈值作出局部决策并上传融合中心(FC),FC利用OR-决策规则作出最终决策。实验结果表明,该方案的决策阈值能够随噪声自适应调整,有效提高了检测率,对噪声不确定性具有很好的鲁棒性。 相似文献
13.
基于Toeplitz矩阵的酉变换波达角估计算法 总被引:1,自引:0,他引:1
为提高Toeplitz矩阵重构算法的估计性能、降低计算量,提出了基于Toeplitz矩阵的酉变换DOA估计算法UHT-MUSIC。该算法在保持估计性能不变的前提下,先将Toeplitz型协方差矩阵变换为Hermition矩阵,然后利用酉变换将其转换为实数矩阵。在此基础上利用MUSIC算法进行DOA估计,其特征值分解及谱峰搜索的计算量降低到同条件下TOEP-MUSIC算法的1/4。同时该算法还有效降低了信源的相关系数,从而提高了算法的分辨性能。仿真实验验证了该算法的正确性。 相似文献
14.
Coupled principal component analysis 总被引:1,自引:0,他引:1
A framework for a class of coupled principal component learning rules is presented. In coupled rules, eigenvectors and eigenvalues of a covariance matrix are simultaneously estimated in coupled equations. Coupled rules can mitigate the stability-speed problem affecting noncoupled learning rules, since the convergence speed in all eigendirections of the Jacobian becomes widely independent of the eigenvalues of the covariance matrix. A number of coupled learning rule systems for principal component analysis, two of them new, is derived by applying Newton's method to an information criterion. The relations to other systems of this class, the adaptive learning algorithm (ALA), the robust recursive least squares algorithm (RRLSA), and a rule with explicit renormalization of the weight vector length, are established. 相似文献
15.
光电跟踪系统的估计性能随着探测概率的下降而降低,是否存在一个临界探测概率,当跟踪系统的实际探测概率高于临界探测概率时,跟踪系统统计意义下的估计误差协方差对任意估计初值均收敛是跟踪系统设计时的一个关键问题。本文证明了跟踪系统临界探测概率的存在性,并且给出了临界探测概率的一组上下界,其上界被描述成一个非线性矩阵不等式(NMI)的最优解,其下界仅与跟踪系统状态转移矩阵的特征值有关。进一步利用摄动线性化方法给出了求解临界探测概率上界的一种迭代线性矩阵不等式(ILMI)算法,并且对跟踪系统在三种标准测试场景中的临界探测概率上界进行了仿真求解,仿真结果表明:当跟踪系统的探测概率高于50%时,其统计意义下的估计误差协方差对任意估计初值均收敛,这为跟踪系统探测概率的设计提供了理论依据。 相似文献
16.
A new multivariate volatility model where the conditional distribution of a vector time series is given by a mixture of multivariate normal distributions is proposed. Each of these distributions is allowed to have a time-varying covariance matrix. The process can be globally covariance stationary even though some components are not covariance stationary. Some theoretical properties of the model such as the unconditional covariance matrix and autocorrelations of squared returns are derived. The complexity of the model requires a powerful estimation algorithm. A simulation study compares estimation by maximum likelihood with the EM algorithm. Finally, the model is applied to daily US stock returns. 相似文献
17.
目的 针对含少量离群点的噪声点云,提出了一种Voronoi协方差矩阵的曲面重建方法。方法 以隐函数梯度在Voronoi协方差矩阵形成的张量场内的投影最大化为目标,构建隐函数微分方程,采用离散外微分形式求解连续微分方程,从而将曲面重建问题转化为广义特征值求解问题。在点云空间离散化过程中,附加最短边约束条件,避免了局部空间过度剖分。并引入概率测度理论定义曲面窄带,提高了算法抵抗离群点能力,通过精细剖分曲面窄带,提高了曲面重建精度。结果 实验结果表明,该算法可以抵抗噪声点和离群点的影响,可以生成不同分辨率的曲面。通过调整拟合参数,可以区分曲面的不同部分。结论 提出了一种新的隐式曲面重建方法,无需点云法向、稳健性较强,生成的三角面纵横比好。 相似文献
18.
Unified eigen analysis on multivariate Gaussian based estimation of distribution algorithms 总被引:1,自引:0,他引:1
Multivariate Gaussian models are widely adopted in continuous estimation of distribution algorithms (EDAs), and covariance matrix plays the essential role in guiding the evolution. In this paper, we propose a new framework for multivariate Gaussian based EDAs (MGEDAs), named eigen decomposition EDA (ED-EDA). Unlike classical EDAs, ED-EDA focuses on eigen analysis of the covariance matrix, and it explicitly tunes the eigenvalues. All existing MGEDAs can be unified within our ED-EDA framework by applying three different eigenvalue tuning strategies. The effects of eigenvalue on influencing the evolution are investigated through combining maximum likelihood estimates of Gaussian model with each of the eigenvalue tuning strategies in ED-EDA. In our experiments, proper eigenvalue tunings show high efficiency in solving problems with small population sizes, which are difficult for classical MGEDA adopting maximum likelihood estimates alone. Previously developed covariance matrix repairing (CMR) methods focusing on repairing computational errors of covariance matrix can be seen as a special eigenvalue tuning strategy. By using the ED-EDA framework, the computational time of CMR methods can be reduced from cubic to linear. Two new efficient CMR methods are proposed. Through explicitly tuning eigenvalues, ED-EDA provides a new approach to develop more efficient Gaussian based EDAs. 相似文献