High-resolution time delay estimation via sparse parameter estimation methods

摘要

This study addresses the high-resolution time delay estimation (TDE) via sparse parameter estimation methods. Two representative algorithms, Sparse Asymptotic Minimum Variance (SAMV) and SParse Iterative Covariance-based Estimation are devised in both the time and frequency domains for application to the TDE of spread-spectrum signals and their performances are analysed in various multipath environments. The authors also proposes the combined approach of SAMV and weighted RELAX, referred to as SAMV-WRELAX, to reduce the computational load. Numerical examples demonstrate that the frequency-domain approaches with a proper type of snapshots not only outperform the corresponding time-domain approaches but also mitigate the problem of the noise correlation encountered in time-domain processing. They also show that the computational load of SAMV-WRELAX with a grid size of Tc decreases up to a few tenths of that of SAMV with a fine grid, e.g. a size of Tc, without any performance degradations.