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Recursive blind identification of non-Gaussian time-varying AR model and application to blind equalisation of time-varying channel

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3 Author(s)
Y. Zheng ; Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore ; Z. Lin ; Y. Ma

A novel method for the blind identification of a non-Gaussian time-varying autoregressive model is presented. By approximating the non-Gaussian probability density function of the model driving noise sequence with a Gaussian-mixture density, a pseudo maximum-likelihood estimation algorithm is proposed for model parameter estimation. The real model identification is then converted to a recursive least squares estimation of the model time-varying parameters and an inference of the Gaussian-mixture parameters, so that the entire identification algorithm can be recursively performed. As an important application, the proposed algorithm is applied to the problem of blind equalisation of a time-varying AR communication channel online. Simulation results show that the new blind equalisation algorithm can achieve accurate channel estimation and input symbol recovery

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IEE Proceedings - Vision, Image and Signal Processing  (Volume:148 ,  Issue: 4 )