Time-Variant Maximum Likelihood Channel Estimation in Mobile Radio Navigation Systems

Abstract

This paper presents a novel time-variant ML channel estimator for mobile radio navigation receivers. Our novel ML channel estimator enables the coherent noise averaging over several hundred codewords for time-variant channel phasors. Compared to the conventional incoherent summation of log-likelihood functions or compared to the conventional time-invariant log-likelihood functions, we avoid the squaring loss (SL) completely. The novel time-variant log-likelihood function compared to the conventional time-invariant log-likelihood function yields an SNR gain of up to 15 dB for an observation interval of 200ms. Additionally, since the novel time-invariant log-likelihood functions only require a Slepian subspace of a small dimension, the computational complexity of our novel time-variant ML channel estimation does not exceed the computational complexity of the conventional time-invariant ML channel estimation.