Subspace-based parameter estimation of symmetric non-causal autoregressive signals from noisy measurements

The notion of Symmetric Non-causal Auto-Regressive Signals (SNARS) arises in several, mostly spatial, signal processing applications. In this paper we introduce a subspace fitting approach for parameter estimation of SNARS from noise-corrupted measurements. We show that the subspaces associated with a Hankel matrix built from the data covariances contain enough information to determine the signal parameters in a consistent manner. Based on this result we propose a MUSIC (Multiple Signal Classification)-like methodology for parameter estimation of SNARS. Compared with the methods previously proposed for SNARS parameter estimation, our SNARS-MUSIC approach is expected to possess a better trade-off between computational and statistical performances.