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Theory Of Blind Source Separation And Its Application In Communication Systems

Posted on:2010-08-07Degree:DoctorType:Dissertation
Country:ChinaCandidate:E F WangFull Text:PDF
GTID:1118360302465522Subject:Information and Communication Engineering
Abstract/Summary:PDF Full Text Request
Human obtains the ability to understand and change the world by acquiring and analyzing the mass information from the nature and our daily lives. However, most of the time, the necessary information is unknown. In signal processing, we abstract these unknown subjects as source signal. With the complete or partial knowledge of the source signal, the source can be extracted signal according to some proper transform of the known information. Blind source separation (BSS) is regards as the process of separating and recovering source signal based on the limited observed signals, without the knowledge of source signal and channel information.Since Herault and Jutten have found the neural network method to realize the blind source separation for speech signals, a great many achievements emerge based on the BSS theoretical research whose application has succeed in the fields of speech recognition, image processing, seismic exploration, biomedical science and etc, as attracts the common attention from the circles of signal processing and neural network. With the promotion of the BSS application process, the problem of the algorithm is being clearer. It has important significances that to deeply research on the BSS algorithm and to find out a practical technology for the performance improving, as well as to further widen the application field of the BSS. This article is based on the hot subject, and pays more attention to the convolved mixture model and the practical BSS technology which increases the robust speaker feature and decreases the cost of receiving equipments.The signal transmission models for narrowband and broadband wireless communication system can be abstracted as BSS linear instantaneous mixture model and linear convolution mixture model, which can be transformed to each other by Fourier and its inverse transform. Researchers had studied several solutions to improve the BSS algorithm performance based on different angles. Generally, the questions being to promote now are based on the following parts: how to overcome the influence of the transmission delay in the model and solve the convolved mixture signals; how to cancel the influence of the additive white Gaussian noise to make sure the performance of the algorithm; how to separate more mixture signals with less use of receiving array elements.This paper studies the linear convolution mixture model based on the analysis of instantaneous mixture model. There are some assumptions for BSS problems, among which the independence assumption is the core. The independence component analysis theorem which is based on this assumption is equivalent to BSS when used to analyze independent source signal. Similarity coefficient and performance index are two parameters usually used to evaluate the performance of the separation algorithm, defined from the aspects of signal separation and matrix separation.BSS problem consists of separation criterion and optimization. The criterion can be based on information theory, maximum SNR or high order cumulants. The nature gradient optimization algorithm is used to simulate the instantaneous mixture BSS algorithm under these three criterions. The result shows that the criterion based on high order cumulants is robust under high Gaussian noise, and hence more suitable to wireless communication system. When the eigenvalues of the weighted covariance matrix are very close, the algorithm based on high order cumulants criterion is more likely to be stuck in local optimum and fail to separate signal. The sequential blind extraction algorithm based on normalized kurtosis. And the algorithm can extract the expected signal with certain properties from the combined signals.The linear convolution mixture model can be transformed to the linear instantaneous mixture model by Fourier transform. Therefore, there are two ways to solve the convolution, i.e., in time domain or frequency domain. Time domain separation algorithm is complicated and has moderate convergent performance, while frequency domain algorithm makes use of FFT, but needs to handle the inconsistent ordering problem of separated sub-signals. The frequency domain separation model shows the contradiction between noise elimination and signal separation. According to the analysis of mixture matrix structure under noiseless assumption, this paper finds out the reason of the inconsistent ordering and proposes two schemes as the solution. A neighbor frequency breadth-angle ratio is defined to find out the wrong frequency points and correct the separated sub-signals'order correspondingly. Coupling operator method introduces coupling factor to maintain the correlation of the separation matrix on the neighbor frequency without separate ordering process, as decreases the probability of ordering chaos. The simulation result shows that although it performs no better than neighbor frequency breadth-angle ratio, it avoids extra computation consumption, which can be applied in the case when high accuracy is not required.As a whole, because of some performance restricting factors of application in real systems, BSS algorithm is still in the stage of theory study. On one hand, receiving equipment of communication systems is under AWGN condition, so either higher-order cumulant or subspace theory only has limited noise suppression ability. In this paper, the time-frequency analysis preprocessing is applied as noise preprocessing method to increase SNR of received signals and separate blind source. A grading noise preprocessing scheme and a joint time-frequency two-step noise cancellation preprocessing scheme are designed to make full use of empirical mode decomposition's fast convergence advantage and wavelet transform's stable performance advantage. Two-step preprocessing scheme can achieve desired SNR of observed signals and improve the noise immunity performance of the algorithm. On the other hand, in most cases, the system is a black box, so that is impossible to design the size of a receiving sensor array according to the number of source signals, even if the number is known. If the number of source signals is too large, the number of sensor elements will be increased correspondingly. In order to simplify the complexity of receiving equipment, study on BSS based on low number of sensor technology is very necessary. Based on the sparse component analysis, this paper has proposed a mixture clustering algorithm combining K-mean clustering algorithm and Kohonen network clustering algorithm. The proposed algorithm focuses on the implementation of clustering, not only increases the convergence speed of Kohonen network, but also achieves accurate clustering. Combined with principle component analysis, K-K-P underdetermined BSS based on mixture clustering algorithm is proposed in this paper, which can estimate the channel clustering matrix accurately and implement BSS based on low number of sensor. Some important research results are given on algorithm practicability and reducing equipment consumption.BSS has been applied in many fields successfully, but the BSS algorithm applied in communication system is constrained by some practical factors. As a discussion for application, this paper applied BSS to rice fading channel, explained the effects of multipath on separation performance by simulation test.
Keywords/Search Tags:Blind Source Separation, Neighbor frequency breadth-angle ratio, Wavelet transform, Empirical Mode Decomposition, Sparse Component Analysis
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