Estimation of nominal direction of arrival and angular spread using an array of sensors

The problem of estimating nominal direction of arrival and angular spread of a source surrounded by a large number of local scatterers using a linear array of sensors is addressed. The maximum likelihood (ML) esti-mator is considered together with an estimator based on least squares t of sample covariance to theoretical covariance matrix derived from a parametric model of the situation. Analysis of the least squares based algorithm is carried out. It is shown that although the least squares estimate is not eecient, it is less computation-ally demanding than the ML approach and provides good enough estimates to initialize the ML estimator. Finally the described method is applied to experimental data.