欠定盲源分离算法研究

【作者】 冯涛；

【导师】 朱立东；

【作者基本信息】 电子科技大学 ， 通信与信息系统（专业学位）， 2012， 硕士

【摘要】 盲源分离是当前信号处理的一个新的研究方向，在不需要知道源信号和信道条件下可以有效地对源信号进行估计，在通信、语音信号处理、生物医学信号处理等方面具有广阔的应用前景。而欠定情况下的盲源分离又是盲信号分离中的一个热点与难点问题，本文对这一问题进行了研究与探讨。本文在第一章中介绍了研究背景及课题意义后，在第二章中研究和讨论了经典的盲源分离算法及常见的欠定盲源分离算法。第三章中考虑为了有效估计混合矩阵并恢复出源信号，结合到现实中的很多信号都是带限信号，作者提出了采用互补滤波器组进行频带分解的欠定盲源分离方法。该方法将接收的混合信号经互补滤波器组分离到不同的子频带，然后在每一个子频带分别估计混合矩阵进行常规的盲分离，利用聚类分析方法估计总的混合矩阵，最后把相关的分离子频带信号进行叠加以恢复出源信号。即使源信号数目远大于观测信号数，但只要保证在每一个子频带的信号数不大于观测信号数，该方法一样可以有效地分离出源信号。最后通过仿真验证了该方法的有效性及良好的分离性能。第四章中就已有经典算法进行了改进。两步策略是当前欠定盲信号分离的基本方法，混叠矩阵的估计是恢复源信号的先决条件。作者考虑在混合矩阵已经估计的前提下，通过矩阵子空间的方法估计源信号，并且利用信号相邻采样点的相关特性，联合判断任意时刻信号的归属，提高欠定盲源分离算法的抗噪能力。最后通过仿真实验验证了该算法的性能及实用性。就DS-CDMA系统在用户数未知情况下，采用独立成分分析(ICA)方法进行盲多用户检测，无法有效进行主成分分析(PCA)及无法判定分离分量的性质。针对这一问题，作者在第五章中提出了基于分离成分中源信号数估计的盲多用户检测算法，先根据DS-CDMA的系统用户容量进行前期PCA处理，然后进行ICA处理，对于ICA分离得到的独立信号，根据其标准峭度（或负熵）判定其为有用信号或噪声信号，完成用户数的估计并且重新对初始PCA的保留路径数进行设置，同时对有用信号进行硬判决，完成有用信息的提取。相对于已有的源信号数估计方法，该算法可以更准确地估计系统中用户数量并能够完成分离信号性质的判定。最后本文通过仿真证明了该算法的良好估计性能。第六章对全文进行了总结并对下一步工作进行了展望。更多还原

【Abstract】 Currently, Blind Source Separation (BSS) is a new research direction in signalprocessing. Without the need to know the source signals and the channel characteristics,the source signals can be estimated effectively. This method has broad applicationprospects in communication, speech signal processing, and biomedical signalprocessing, and so on. Under-determined Blind Source Separation (UBSS) is achallenging and hot topic in BSS, this dissertation conducts a study and discussion onthis issue.Based on the above background, the first chapter describes the researchbackground and significance of the subject, and the second chapter gives a study anddiscussion of the classic BSS algorithms and common UBSS algorithms.Considering so many signals in real world are band-limited, a newunder-determined BSS method is proposed in the third chapter using frequencydecomposition method by complementary filters to estimate mixing matrix and recoversource signals effectively. The sensing signals are divided into different sub-bands, andeach sub-band uses common BSS algorithm. We can reconstruct the source signals bycombing the related signals together. Even if the number of source signals is muchlarger than that of sensing signals, this method could also recover the source signalswhen the number of source signals are not more than that of sensing signals in eachsub-band. Finally, computer simulation confirms the validity of this method.A classical UBSS algorithm has been improved in the forth chapter. Two-stepstrategy is the basic method of under-determined blind source separation currently, theestimation of mixing matrix is a prerequisite for recovering the source signals. Weconsider the situation that the mixing matrix has been estimated, matrix subspacemethod is applied to estimate source signals. In order to improve anti-noise capability ofthe underdetermined blind source separation algorithm, the relevant characteristics ofadjacent sampling points of signals is used to determine the ownership of the signals atany time. Finally, simulations demonstrate the performance and validity of the proposedalgorithm. The fifth chapter considers the case of the unknown number of users in DS-CDMAsystem, independent component analysis (ICA) based blind multiuser detection (MUD)cannot effectively implement the principal component analysis (PCA) and determine thenature of the independent component. To solve this problem, this dissertation proposesan algorithm for blind MUD based on estimation of the number of source signals inseparated components. First, pre-PCA is applied based on the DS-CDMA systemcapacity, and then ICA processing is used. The ICA independent signals are determinedas useful signal or noise signal according to standard kurtosis (or negative entropy). Theestimation of the number of users is used to reset the retain number of paths of PCA.Additionally, the useful signals are sent to the hard decision to complete the usefulinformation extraction. Compared with the existed source number estimation methods,this algorithm can more accurately estimate the number of users in system anddetermine the nature of the separated signals. Finally, the simulation confirms the goodestimation performance of the proposed algorithm.The last chapter is the summation of this dissertation and future work isprospected.更多还原