基于HM-GMM的MIMO-AR混合物非平稳源分离与系统辨识

2017 First International Conference on Electronics Instrumentation & Information Systems (EIIS) Pub Date : 2017-06-01 DOI:10.1109/EIIS.2017.8298650

Jiong Li, Hang Zhang, Menglan Fan

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引用次数: 0

摘要

提出了一种多输入多输出自回归(MIMO-AR)混合信号的非平稳源分离技术。本文采用隐马尔可夫高斯混合模型(HM-GMM)表示源模型。首先采用最大期望算法实现MIMO-AR模型辨识，然后通过矩阵联合对角化实现盲源分离。仿真结果表明了该方法的有效性，不仅适用于系统辨识，而且适用于BSS。

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Non-stationary source separation and system identification for MIMO-AR mixtures using HM-GMM

A technique for non-stationary source separation is proposed for multiple-input multiple-output auto-regressive (MIMO-AR) mixtures. Hidden Markov Gaussian mixture model (HM-GMM) is employed in this paper to represent source model. Expectation-maximum algorithm is used to achieve MIMO-AR model identification, and then blind source separation (BSS) is achieved by matrix joint diagonalization. Simulation results show its effectiveness, not only for system identification, but also for BSS.

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2017 First International Conference on Electronics Instrumentation & Information Systems (EIIS)

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