Estimation of autoregressive fading channels based on two cross-coupled H ∞ filters

Abstract

This paper deals with the on-line estimation of time-varying frequency-flat Rayleigh fading channels based on training sequences and using H ∞ filtering. When the fading channel is approximated by an autoregressive (AR) process, the AR model parameters must be estimated. As their direct estimations from the available noisy observations at the receiver may yield biased values, the joint estimation of both the channel and its AR parameters must be addressed. Among the existing solutions to this joint estimation issue, Expectation Maximization (EM) algorithm or crosscoupled filter based approaches can be considered. They usually require Kalman filtering which is optimal in the H 2 sense provided that the initial state, the driving process and measurement noise are independent, white and Gaussian. However, in real cases, these assumptions may not be satisfied. In addition, the state-space matrices and the noise variances are not necessarily accurately estimated. To take into account the above problem, we propose to use two crosscoupled H ∞ filters. This method makes it possible to provide A. Jamoos (B)

Estimation of Autoregressive Parameters from Noisy Observations Using Iterated Covariance Updates

“…Another approach to this problem makes use of coupled Kalman filters [ 104 ] or cross-coupled filters [ 105 , 106 ] has been developed. In essence these move beyond the Kalman filter approach referenced above ([ 21 ], Section 3.3) and operate as follows.…”