Estimation and prediction of noise power based on variational Bayesian and adaptive ARMA time series

Jingyi Zhang; Yonggui Li; Yonggang Zhu; Binwu Li

doi:10.1117/12.2064180

16 April 2014 Estimation and prediction of noise power based on variational Bayesian and adaptive ARMA time series

Jingyi Zhang, Yonggui Li, Yonggang Zhu, Binwu Li

Proceedings Volume 9159, Sixth International Conference on Digital Image Processing (ICDIP 2014); 91590O (2014) https://doi.org/10.1117/12.2064180
Event: Sixth International Conference on Digital Image Processing, 2014, Athens, Greece

Abstract

Estimation and prediction of noise power are very important for communication anti-jamming and efficient allocation of spectrum resources in adaptive wireless communication and cognitive radio. In order to estimate and predict the time-varying noise power caused by natural factors and jamming in the high frequency channel, Variational Bayesian algorithm and adaptive ARMA time series are proposed. Through establishing the time-varying noise power model, which controlled by the noise variance rate, the noise power can be estimated with Variational Bayesian algorithm, and the results show that the estimation error is related to observation interval. What’s more, through the analysis of the correlation characteristics of the estimation power, noise power can be predicted based on adaptive ARMA time series, and the results show that it will be available to predict the noise power in next 5 intervals with the proportional error less than 0.2.

Citation Download Citation

Jingyi Zhang, Yonggui Li, Yonggang Zhu, and Binwu Li "Estimation and prediction of noise power based on variational Bayesian and adaptive ARMA time series", Proc. SPIE 9159, Sixth International Conference on Digital Image Processing (ICDIP 2014), 91590O (16 April 2014); https://doi.org/10.1117/12.2064180

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