基于核密度估计的信号盲处理

在信号的盲处理中，常用一个非线性函数来代替算法中不可知的评价函数。针对不同统计特性的源信号，需要选择不同的非线性函数。核密度估计法可以用来对评价函数直接做出估计，从而避免了对非线性函数的选择。它使得盲处理算法可以成功地恢复出包含不同统计特性的杂系混合信号。因此，它更加接近于实际的应用。     

一种任意信号源盲分离的高效算法

提出了信号源盲分离的DBBSS算法.利用随机变量概率密度函数非参数估计的核函数法,对混合信号的概率密度函数及其导数进行估计,并由此估计信号的评价函数(score function).解决了现有信号源盲分离算法中,普遍存在的非线性函数只能凭经验选取,以及混合信号同时包含超高斯信号和亚高斯信号时,算法失效的问题.该方法非常简单,可以直接应用于所有以非线性函数代替评价函数的信号源盲分离算法.仿真结果验证了算法的有效性.     

基于批处理和核函数的非线性盲源分离算法

针对基于核函数的非线性盲源分离算法性能对核函数及其参数选择依赖性强这一问题,提出采用批处理方法代替聚类和核主成分分析方法来构造低维近似子空间的正交基,以改进基于核函数的非线性盲源分离算法对核函数及其参数变化的稳健性,并对这种改进的非线性盲源分离算法进行了完整的分析.通过仿真实验,对分离信号与源信号求相似度,可以看到提出...     

基于NPCA的盲源分离算法

主要讨论了基于非线性主分量分析(NPCA)的盲源分离,从理论与实验2个方面详细分析了算法的特性与效果。针对算法中的非线性函数选择的问题,采用了在线统计的方法,即根据不同的输入信号选择不同的非线性函数。从实验结果可以看出,该方法不仅可以很好地解决源信号为亚高斯信号混合的盲源分离问题,而且对源信号为亚高斯和超高斯信号混合的盲源分离问题也取得了很好的效果。     

基于遗传算法的超亚高斯混合信号盲分离

针对基于扩展信息最大化算法的盲源分离算法在分离超亚高斯混合信号时依赖于信号的峭度估计且对初始分离矩阵和步长较为敏感的问题,提出了一种基于遗传算法的盲源分离算法。该算法以分离信号之间的互信息作为代价函数,采用非多项式函数的逼近方法解决了互信息求解过程中涉及到的负熵的计算问题,用遗传算法代替梯度寻优算法最小化代价函数。仿真结果表明:在分离超亚高斯混合信号时,该算法计算简单,鲁棒性好,迭代100次时性能指数值达到0.025 5,分离性能优于基于扩展信息最大化算法的盲源分离算法。     

基于盲波束形成的分布式目标波达方向估计方法

在分布式目标波达方向估计问题中,空间信号分布函数一般具有共轭对称性.本文提出了一种新的盲波束形成方法,不用进行多维参数搜索,不要求已知空间信号分布的具体函数形式,并适用于分布函数形式不同的分布式目标同时存在的情况.仿真实验结果表明,这种方法的波达方向估计性能对信号源的分布特性不敏感.     

非线性功放信道下联合信道估计研究

为了解决非线性放大器在60 GHz毫米波信道中造成的非线性影响,提出了基于马尔科夫蒙特卡洛（Markov Chain Monte Carlo,MCMC）算法的联合信道估计与信号检测技术。采用的是MCMC算法中的Metropolis-Hastings方法,在非线性放大器及信道参数未知的情况下,通过被非线性和噪声污染的输出信号（观测信号）来估计非线性放大器的参数,检测输入信号被称为盲均衡技术。仿真结果给出了非线性参数与真实值的对比图以及随SNR变化的误比特率,性能优越。     

基于SCS算法的盲自适应多用户检测器

提出了一种基于ＳＣＳ（Ｓｏｆｔ－Ｃｏｎｓｔｒａｉｎｔ－Ｓａｔｉｓｆａｃｔｉｏｎ）算法的盲自适应多用户检测器。基于ＳＣＳ算法的盲自适应多用户检测器只需知道期望用户的扩频码及定时信息,而且通过自动选择用来估计期望信号的非线性函数,可以调节算法的收敛速度和估计误差。仿真实验表明该方法具有较好的抗多址干扰的能力。     

PN序列估计与扩频隐藏信息分析

在非协作信息侦测情况下,提出了一种直接序列扩频(DS-SS)信号PN序列的估计方法,在此基础上实现了扩频隐藏信息的盲提取.该方法以估计序列和扩频信号的累积相关值为目标函数,建立估计序列长度及其构成的两变量优化模型,通过遗传算法求解,可较好地估计出PN序列.通过获取的PN序列与藏密信号的相关性分析,可实现扩频隐藏信息的盲...     

空时分组码通信中的一类ICA盲检测方案

空时分组码（STBC）通过使用发射分集策略和空时编码方案可以明显改善系统性能。然而，其接收端必须获得准确的信道状态信息（CSI）才能进行有效的信号检测。而对于复杂的无线通信环境，这种前提条件有时却难以得到满足。独立分量分析（ICA）是一种将一个复杂的数据集合分解为多个独立子集的盲源分离（BSS）技术。通常情况下，即使没有空间信道的任何信息，ICA也可以仅凭接收信号恢复出发射信号。提出了一种利用ICA技术的STBC盲信号检测方案，在建立了适用于ICA的特定通信系统模型后，几种典型的ICA算法被用来进行性能比较。理论分析表明，ICA盲接收技术的应用可以在一定程度上替代基于信道估计的传统方法，增强系统对信道估计错误的顽健性。仿真实验结合了具体的STBC系统，比较了基于ICA的不同方案的性能，并讨论了最优的信号检测方案。     

Blind Source Separation Based on Nonparametric Density Estimation

A nonparametric density estimation method is used to directly estimate the score functions encountered in relative gradient (or natural gradient) adaptation algorithms in the blind source separation problem. Compared to the method where simple nonlinear functions are used to replace the unknown score functions, the key advantage of the direct estimation of the score functions lies in the fact that it enables the algorithm to separate hybrid mixtures of sources that contain both super-Gaussian and sub-Gaussian signals. The source statistics required for the choices of the nonlinear functions is no longer needed, because the score functions are directly estimated. The algorithm is thus expected to be applicable to more blind cases.     

未知源信号峭度正负性的盲分离

大多数的盲分离算法假设源信号峭度的正负性是己知的，并据此选择相应的非线性函数近似评价函数(score function)。针对源信号峭度的正负性未知的情况，本文提出了一个评价函数的参数估计方法，本算法能有效地分离混合在一起的超高斯信号和亚高斯信号，仿真结果验证了算法的有效性。     

有效的自适应波达方向盲估计算法

本文在分析自适应信号盲分离算法渐近稳定性基础上,提出了一种有效的自适应学习算法用于波达方向盲估计。研究了算法的有界性和渐近稳定性。以渐近稳定性为前提,给出了算法中非线性函数的适当选择。为了抑制噪声和估计信源数,在算法中还增加了白化过程。仿真研究表明,算法是有效的而鲁棒的,其能够从有操声的阵元信号中估计波达方向。     

Blind equalization of nonlinear channels from second-orderstatistics

This paper addresses the blind equalization problem for single-input multiple-output nonlinear channels, based on the second-order statistics (SOS) of the received signal. We consider the class of "linear in the parameters" channels, which can be seen as multiple-input systems in which the additional inputs are nonlinear functions of the signal of interest. These models include (but are not limited to) polynomial approximations of nonlinear systems. Although any SOS-based method can only identify the channel to within a mixing matrix (at best), sufficient conditions are given to ensure that the ambiguity is at a level that still allows for the computation of linear FIR equalizers from the received signal SOS, should such equalizers exist. These conditions involve only statistical characteristics of the input signal and the channel nonlinearities and can therefore be checked a priori. Based on these conditions, blind algorithms are developed for the computation of the linear equalizers. Simulation results show that these algorithms compare favorably with previous deterministic methods     

PARAFAC-based channel estimation and data recovery in nonlinear MIMO spread spectrum communication systems

In this paper, a new tensorial modeling is first proposed for nonlinear multiple-input multiple-output (MIMO) direct sequence spread spectrum communication systems. The channel is modeled as an instantaneous MIMO Volterra system. Then, a direct data approach for joint blind channel estimation and data recovery is developed using the parallel factor (PARAFAC) decomposition of a third-order tensor composed of received signals, exploiting space, time and code diversities. A blind channel estimation method based on the PARAFAC decomposition of a fifth-order tensor composed of covariances of the received signals is also proposed, considering phase shift keying (PSK) modulated transmitted signals. The proposed estimation algorithms are evaluated by simulating a nonlinear uplink MIMO radio over fiber (ROF) communication system.     

Blind channel estimation via combining autocorrelation and blind phase estimation

Symbol spaced blind channel estimation methods are presented which can essentially use the results of any existing blind equalization method to provide a blind channel estimate of the channel. Blind equalizer's task is reduced to only phase equalization (or identification) as the channel autocorrelation is used to obtain the amplitude response of the channel. Hence, when coupled with simple algorithms such as the constant modulus algorithm (CMA) these methods at baud rate processing provide alternatives to blind channel estimation algorithms that use explicit higher order statistics (HOS) or second-order statistics (subspace) based fractionally-spaced/multichannel algorithms. The proposed methods use finite impulse response (FIR) filter linear receiver equalizer or matched filter receiver based infinite impulse response+FIR linear cascade equalizer configurations to obtain blind channel estimates. It is shown that the utilization of channel autocorrelation information together with blind phase identification of the CMA is very effective to obtain blind channel estimation. The idea of combining estimated channel autocorrelation with blind phase estimation can further be extended to improve the HOS based blind channel estimators in a way that the quality of estimates are improved.     

A Newton-like algorithm for complex variables with applications inblind equalization

Although the Newton algorithm has been extended to the complex domain in different forms, none of them seems to be directly applicable to blind equalization. Therefore, the objective of this correspondence is to develop an algorithm for blind equalization in the complex domain. We propose a Newton-like algorithm based on a complex Taylor series. Stochastic Newton-like algorithms (SNLA) for two blind equalization cost functions are developed. Simulations show that the new algorithms perform slightly better than the self-orthogonalizing algorithm     

A Non-Linear Blind Source Separation Method Based on Perceptron Structure and Conjugate Gradient Algorithm

The linear mixing model has been considered previously in most of the researches which are devoted to the blind source separation (BSS) problem. In practice, a more realistic BSS mixing model should be the non-linear one. In this paper, we propose a non-linear BSS method, in which a two-layer perceptron network is employed as the separating system to separate sources from observed non-linear mixture signals. The learning rules for the parameters of the separating system are derived based on the minimum mutual information criterion with conjugate gradient algorithm. Instead of choosing a proper non-linear functions empirically, the adaptive kernel density estimation is used in order to estimate the probability density functions and their derivatives of the separated signals. As a result, the score function of the perceptron’s outputs can be estimated directly. Simulations show good performance of the proposed non-linear BSS algorithm.