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NSCT的独立分量分析方法

为了提高独立分量分析（ICA）算法的分离性能,提出了基于非下采样轮廓波变换（NSCT）的ICA方法。首先对混合图像进行NSCT,使混合图像的非高斯性在NSCT域得以增强,然后在变换域求解分离矩阵。理论分析和仿真实验均表明,该方法可以获取更好的分离精度和更快的收敛速度。     

基于FastICA的雷达信号分选研究

现代战争中新体制雷达的大量涌现,电磁环境变得越来越复杂,对雷达信号分选提出了新的挑战。目前的雷达信号分选领域,多采用基于参数容差的传统分选方法,这些方法受参数误差的影响大,对PDW参数相似的雷达无法分选,已经无法适应复杂电磁环境。在对FastICA算法原理分析的基础上,重点研究了将它应用于PDW参数相近的雷达信号和参差脉冲列的分选,并进行了仿真。仿真结果表明,FastICA是建立在源信号统计独立基础上的处理,对信号相关性敏感,受参数误差的影响小,可以有效解决上述问题,为雷达信号分选提供了一种新的思路。     

Noise separation of the yarn tension signal on twister using FastICA

Some unknown noise will influence the wave appearance of the twist yarn tension signal in different factory environments or production processes. It is difficult to analyse the tension signal pattern and recognize unusual defects by the on-line yarn quality control system. In this paper, the independent component analysis (FastICA: fixed-point independent component analysis) is applied to separate the unknown noise signal from the unusual tension signal on the yarn twist machine. Different from the traditional low-pass filter (e.g., Butterworth filter), FastICA can not only successfully separate the noise with different types but also remain the main tension signal information. Firstly, FastICA algorithm is introduced, and then simulation experiments and on-line tests are carried out to evaluate the performance of this method and traditional low-pass filter.     

一种改进的基于多用户检测的独立分量分析算法

为了降低FastICA算法的计算复杂度,提出了一种基于多用户检测串行干扰抵消的新型独立分量分析算法MUD_FastICA。该算法结合了盲信号分离和多用户检测串行干扰抵消两种信号处理技术,利用减法和低维特征值分解来保证每次分离出不同独立分量和达到降低算法复杂度的目的。通过分析和仿真可以看出,所提算法在不影响分离性能的前提下,显著降低了算法的迭代次数和每次迭代的计算复杂度。在信噪比0 dB和4个源信号混合情况下,分离第二个信号的迭代次数和所需计算单元分别下降了14%和37%,分离第三个信号的迭代次数和所需计算单元分别下降了22%和58%,因此更加适合对实时性要求高的通信系统。     

Blind separation of piecewise stationary non-Gaussian sources

We address independent component analysis (ICA) of piecewise stationary and non-Gaussian signals and propose a novel ICA algorithm called Block EFICA that is based on this generalized model of signals. The method is a further extension of the popular non-Gaussianity-based FastICA algorithm and of its recently optimized variant called EFICA. In contrast to these methods, Block EFICA is developed to effectively exploit varying distribution of signals, thus, also their varying variance in time (nonstationarity) or, more precisely, in time-intervals (piecewise stationarity). In theory, the accuracy of the method asymptotically approaches Cramér–Rao lower bound (CRLB) under common assumptions when variance of the signals is constant. On the other hand, the performance is practically close to the CRLB even when variance of the signals is changing. This is demonstrated by comparing our algorithm with various methods that are asymptotically efficient within ICA models based either on the non-Gaussianity or the nonstationarity. The benefit of our algorithm is demonstrated by examples with real-world audio signals.     

变电站遥视系统中运动目标检测

运动目标检测是当前无人值守变电站遥视系统的智能视频监控技术需要解决的问题。采用基于负熵最大判据的快速算法(FastICA)又称为定点法(Fixed-point),通过随机梯度法调节分离矩阵W来达到对信号源进行优化的目的。试验表明,在背景亮度发生变化时,FastICA法也可以很好地检测出运动目标,并将其与背景分离,还可确定运动物体的运动轨迹。试验证明了FastICA是一种鲁棒性较强的运动目标检测方法,具有较强的抗图像背景灰度变化的能力。     

基于独立成分分析算法的纺纱锭子噪声测试

纺纱锭子性能的优劣对生产效率和纱线质量具有决定性的影响.以纺纱锭子运转中的噪声信号和驱动电动机单独运转的噪声信号为考察对象,对纺纱锭子运转过程进行分析.利用频谱分析和快速独立成分分析( FastICA)算法将纺纱锭子运转噪声信号的分离信号与驱动电动机单独运转的噪声信号主频率进行对比,38.88％分离主频率与驱动电机单独...     

Face Recognition Based on Gabor Feature Extraction Followed by FastICA and LDA

Over the past few decades, face recognition has become the most effective biometric technique in recognizing people’s identity, as it is widely used in many areas of our daily lives. However, it is a challenging technique since facial images vary in rotations, expressions, and illuminations. To minimize the impact of these challenges, exploiting information from various feature extraction methods is recommended since one of the most critical tasks in face recognition system is the extraction of facial features. Therefore, this paper presents a new approach to face recognition based on the fusion of Gabor-based feature extraction, Fast Independent Component Analysis (FastICA), and Linear Discriminant Analysis (LDA). In the presented method, first, face images are transformed to grayscale and resized to have a uniform size. After that, facial features are extracted from the aligned face image using Gabor, FastICA, and LDA methods. Finally, the nearest distance classifier is utilized to recognize the identity of the individuals. Here, the performance of six distance classifiers, namely Euclidean, Cosine, Bray-Curtis, Mahalanobis, Correlation, and Manhattan, are investigated. Experimental results revealed that the presented method attains a higher rank-one recognition rate compared to the recent approaches in the literature on four benchmarked face datasets: ORL, GT, FEI, and Yale. Moreover, it showed that the proposed method not only helps in better extracting the features but also in improving the overall efficiency of the facial recognition system.     

基于快速独立分量分析的谐波/间谐波频谱分离算法

在非同步采样条件下,若电网采样信号中谐波和间谐波相邻,会出现严重的频谱干涉问题,且无法识别出信号中实际频率成分。针对以上问题,提出了一种基于快速独立分量分析(Fast ICA)的频谱分离算法测量谐波和间谐波参数。首先构建了多频率成分模型,将频谱中的谱线表示为多个频率成分分量的叠加,然后利用Fast ICA算法和最小二乘法得到频率成分参数,最终实现了对相邻多频率成分的测量。仿真结果表明,该算法可以在需求谱线数较少的情况下准确识别频率成分并保持较好的测量精度,且具有一定的抗噪能力。