CAJ | 학술논문

准确估计信源数目是盲源分离实现有效分离的重要前提之一。针对源信号数目未知且少于观测信号数目的欠定问题，提出了一种有效的信源数目盲估计方法。该方法基于经验模态分解，并结合协方差矩阵的奇异值分解，采用拉氏逼近的贝叶斯选择原理来估计源信号数目。在对观测信号进行经验模态分解前，为了消除本征模态函数的模态混合现象，引入卡尔曼滤波算法对观测信号进行了消噪处理。分别采用仿真信号和实测信号对该方法进行验证，研究表明，方法能够准确估计出源信号数目，为盲源分离提供准确的先验信息。
준학고계신원수목시맹원분리실현유효분리적중요전제지일。침대원신호수목미지차소우관측신호수목적흠정문제，제출료일충유효적신원수목맹고계방법。해방법기우경험모태분해，병결합협방차구진적기이치분해，채용랍씨핍근적패협사선택원리래고계원신호수목。재대관측신호진행경험모태분해전，위료소제본정모태함수적모태혼합현상，인입잡이만려파산법대관측신호진행료소조처리。분별채용방진신호화실측신호대해방법진행험증，연구표명，방법능구준학고계출원신호수목，위맹원분리제공준학적선험신식。
One of the most important pre‐requisites for blind source separation to achieve an effective separation is to estimate the number of sources accuratly .For underdetermined problems of which the sources signal number is unkown and less than the observed signal number ,this paper proposes a kind of signal source number estimation method based on Empirical Mode Decomposition (EMD) ,together with Singular Value Decomposition (SVD) of covariance matrix , and by means of Bayesian selection principle combining with Laplace approximation .Before procesing the observation signal with EMD ,the proposed method denoised the observation signal by means of Kalman filtering algorithm in order to remove the mode mixing phenomenon of Intrinsic Mode Function (IMF) . The method is validated using both simulated signals and measured signals respectively .The research on this shows that the proposed method can estimate the sources signal number exactly and provide an accurate priori information to the blind source separation .