DOA estimation by ARMA modelling and pole decomposition

Abstract

A high-resolution DOA-estimation technique is proposed to deal with unknown noise-spatial-covariance structure and unknown array-sensor gain. By modelling the source signals as autoregressive moving-average (ARMA) processes with unknown parameters, a formula is derived which relates the source DOAs with the source poles and array-covariance functions. A virtual data matrix is formed, independent of the sensor-gain uncertainty and noise covariance, and a factorisation of this virtual data matrix shows that the subspace-based techniques can be directly applied to estimate the source DOAs. This technique has the advantage that it requires neither the prior knowledge about the sensor-noise covariance nor the sensor-gain calibration. Simulation results are presented to show the effectiveness of the technique and comparisons with the MUSIC algorithm are also included.