Title :
Bayesian Estimation of Class A Noise Parameters with Hidden Channel States
Author :
Jiang, Yu-Zhong ; Hu, Xiu-lin ; Kai, Xu ; Qi, Zhai
Author_Institution :
Huazhong Univ. of Sci. & Technol., Wuhan
Abstract :
The Middleton Class A interference model is statistical-physical and parametric model for man-made and natural electromagnetic interference. In this letter, the efficient Bayesian estimator of the Class A model parameters is derived and calculated by the Gibbs sampler, a Markov Chain Monte Carlo (MCMC) procedure. The estimator can estimate two-parameter and hidden states for Class A noise model simultaneously. Simulation of this estimator with small sample sizes indicates that this technique is efficient and near-optimal performance.
Keywords :
Bayes methods; Markov processes; Monte Carlo methods; channel estimation; electromagnetic interference; parameter estimation; signal detection; Bayesian estimation; Gibbs sampler; Markov Chain Monte Carlo process; Middleton Class A interference model; electromagnetic interference; hidden channel states; noise parameters; signal detection; Background noise; Bayesian methods; Electromagnetic interference; Gaussian distribution; Gaussian noise; Monte Carlo methods; Parameter estimation; Signal processing algorithms; State estimation; Working environment noise; Impulsive Noise; Middleton Class A Model; Non-Gaussian Noise; Parameter Estimation;
Conference_Titel :
Power Line Communications and Its Applications, 2007. ISPLC '07. IEEE International Symposium on
Conference_Location :
Pisa
Print_ISBN :
1-4244-1090-8
Electronic_ISBN :
1-4244-1090-8
DOI :
10.1109/ISPLC.2007.371088
