Auxiliary particle filtering for detection in ARCH noise

There has been great interest in particle filtering as a sequential Bayesian estimation method for dynamic systems. The problem of detection in flat fading channels in the presence of autoregressive conditional heteroscedasticity (ARCH) noise is addressed. An auxiliary particle filtering (APF) algorithm is presented for the estimation of the transmitted sequence and ARCH noise variance. We introduce the ARCH process for the purpose of noise modelling. The applicability of the model is based on a variance change over time compatible to a large extent with non-stationarity and non-Gaussianity of wireless fading channels. We show, through simulation, the superiority of the proposed algorithm over the bootstrap particle filter (PF) even in the presence of the new noise model; i.e., the ARCH process.