Oblique projection for source estimation in a competitive environment: Algorithm and statistical analysis

In this paper, we study the problem where the aim is to estimate the source (complex amplitude) parameter of a single signal contaminated by a structured interference (constituted by the other signals) and by a background Gaussian noise. To solve this problem, we propose an estimator based on a partially estimated oblique projection. We derive closed-form expressions of the variance of this estimator and of the Cramér–Rao bound (CRB) associated with the considered model. In particular, we show that the proposed estimator is (i) asymptotically (for large number of sensors) efficient in the sense that its variance meets the CRB for a single signal in noise and (ii) for a small of moderate number of sensors, the variance remains close to the CRB without structured interference for well separated bearings.     

基于MIMO-OFDM系统的盲源分离算法研究

为了实现使用OFDM调制的MIMO系统的盲源分离问题,提出了在接收端使用Nyquist频率对接收到的信号进行采样的,从而将原始的MIMO-OFDM系统盲源分离问题转变为N组的独立分量分离的问题.通过将FastICA算法扩展到复数域,在频域中对每一组相同子载波上的信号进行独立分量分析,最终实现信号的盲分离.仿真实验获得的结果证实了该方案具有良好的恢复效果,且算法收敛的迭代次数只需要8～10次.     

基于阵列天线的无线电侦察信号盲分离

盲源分离技术有非常重要的理论意义和实用价值,在信号处理的一些领域得到了广泛应用。提出将盲源分离技术用于无线电侦察系统,实现无线电侦察多信号的盲分离。理论分析和仿真结果表明具有良好的效果。     

混叠通信信号的盲分离处理

同频混叠信号的处理一直是通信信号处理中的难点,常规的信号处理方法难以对其进行有效分析。本文运用最新的盲源分离理论,实现了混叠通信信号的盲分离。仿真结果表明,本文采用的算法分离效果好,运算速度快,可移植性强,基本实现了对混叠通信信号的实时盲分离。     

RESEARCH OF QUANTUM GENETIC ALGORITH AND ITS APPLICATION IN BLIND SOURCE SEPARATION

This letter proposes two algorithns:a novel Quantum Genetic Algorithm(QGA)based on the improvement of Han‘s Genetic Quantum Algorithm(GQA)and a new Blind Source Separation(BSS)method based on QGA and Independent Component Analysis(ICA).The simulation result shows that the efficiency of the new BSS nethod is obviously higher than that of the Conventional Genetic Algorithm(CGA).     

一类基于非线性PCA准则的复数信号盲分离算法

在阵列信号处理过程中，经常遇到复数信号盲分离问题。例如，卷积混合型的源信号的盲分离；声纳信号盲分离。本文提出了一类基于非线性准则的复数信号盲分离算法。将非线性函数引入学习过程，由算法自动调节学习速率。计算机仿真实验验证了算法的有效性，文中给出了验证结果。     

Compound noise separation in digital circuits using blind source separation

Analysis of individual noise sources in pre-nanometer circuits cannot take into account the evolving reality of multiple noise sources interacting with each other. Noise measurement made at an evaluation node will reflect the cumulative effect of all the active noise sources, while individual and relative severity of various noise sources will determine what types of remedial steps can be taken, pressing the need for development of algorithms that can analyze the contributions of different noise sources when a noise measurement is available. This paper addresses the cocktail-party problem inside integrated circuits with multiple noise sources. It presents a method to extract the time characteristics of individual noise source from the measured compound voltage in order to study the contribution and properties of each source. This extraction is facilitated by application of blind source separation technique, which is based on the assumption of statistical independence of various noise sources over time. The estimated noise sources can aid in performing timing and spectral analysis, and yield better circuit design techniques.     

基于复数FastICA算法的短波同频信号盲源分离

盲源分离是一种经典信号处理方法,可广泛应用于通信信号、阵列处理的混合信号有效分离。将一种基于独立分量分析的复数信号盲源分离算法应用于实际的短波信号分离中,能有效地实现对各种信号组合的分离。     

基于奇异值分解的超定盲信号分离

该文研究超定盲信号分离,即观测信号个数不少于源信号个数情况下的盲信号分离问题。作者 从分离矩阵的奇异值分解出发,首先提出一种基于独立分量分析的超定盲信号分离代价函数,接着推导了一般梯度学习算法。此后,借助于相对梯度的概念,证明超定盲信号分离与通常的完备盲信号分离具有相同形式的自然梯度算法。仿真试验验证了算法的有效性。     

Blind source separation of convolutive mixtures of non-circular linearly modulated signals with unknown baud rates

This paper addresses the problem of blind separation of convolutive mixtures of BPSK and circular linearly modulated signals with unknown (and possibly different) baud rates and carrier frequencies. In previous works, we established that the Constant Modulus Algorithm (CMA) is able to extract a source from a convolutive mixture of circular linearly modulated signals. We extend the analysis of the extraction capabilities of the CMA when the mixing also contains BPSK signals. We prove that if the various source signals do not share any non-zero cyclic frequency nor any non-conjugate cyclic frequencies, the local minima of the constant modulus cost function are separating filters. Unfortunately, the minimization of the Godard cost function generally fails when considering BPSK signals that have the same rates and the same carrier frequencies. This failure is due to the existence of non-separating local minima of the Godard cost function. In order to achieve the separation, we propose a simple modification of the Godard cost function which only requires knowledge of the BPSK sources frequency offsets at the receiver side. We provide various simulations of realistic digital communications scenarios that support our theoretical statements.     

基于独立矢量基的波达方向估计

独立分量分析可从线性混合的信号中分离出彼此独立的信号源,也就是利用独立矢量基对混合的信号进行分离,这种方法在一定条件下与盲信源分离等价。该文利用通过非线性最小均方准则获得的独立矢量基,并且根据其所具有的特性,将其应用于均匀线阵的信号波达方向估计。     

基于独立分量分析的PCMA信号盲分离算法

针对非协作通信中成对载波多址(Paired Carrier Multiple Acess,PCMA)信号的盲分离问题,提出了一种基于独立分量分析(Independent component analysis,ICA)的单通道盲分离算法。首先对接收到的单路PCMA信号进行参数估计得到其残余载波频率,再对其处理得到两路基带混合信号,最后利用ICA算法分离出源基带信号。该算法在未知两个卫星地面站发送信号的情况下,从接收到的PCMA信号中恢复出两路源基带信号。仿真实验表明,本文算法在信噪比为-10dB时仍具有良好的分离效果,两路基带信号的波形相似系数可分别达到0.94与0.86以上。     

采用MRSSA-ICA的共信道多通信信号盲分离

针对时频域部分重叠的多个跳频通信信号共信道盲分离问题,提出了一种新的共信道盲分离算法SCBSS(Single Channel Blind Source Separation)。首先,重新定义多分辨奇异谱分析(Multi-resolution Singular Spectrum Analysis,MRSSA)算法,利用其冗余性来重构伪多输入输出模型;接着引入独立分量分析算法用于提取感兴趣的独立分量。仿真结果验证了所提算法分离多个正交相移键控(Quadrature Phase Shift Keying,QPSK)调制的时频域部分重叠跳频通信信号的有效性和鲁棒性,且不需要任何先验。