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 loglikelihood functions or compared to the conventional timeinvariant 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 200 ms. Additionally, since the novel time-invariant log-likelihood functions only require a Slepian subspace of a small dimension, the computational complexity of our novel timevariant ML channel estimation does not exceed the computational complexity of the conventional time-invariant ML channel estimation.

A design study on complexity reduced multipath mitigation

“…Modifications of the CRMM include extension of data size reduction to time-variant signals (Groh et al, 2011), optimized correlator computation (Groh and Sand, 2012), and replacement of the Newton-type optimization with expectation maximization or space alternating general expectation maximization algorithms . In this paper, the focus is on the data size reduction of CRMM and its realizability in hardware in the subsequent sections.…”

“…Several publications on CRMM discuss various implementation aspects of the algorithm, e.g. Groh and Sand (2008); Groh et al (2011); Groh and Sand (2012). However, none provides a more detailed analysis with respect to real-time or hardware implementation issues.…”