Maximum likelihood bearing estimation by quasi-Newton method using a uniform linear array

An efficient algorithm for maximum likelihood (ML) bearing estimation of narrowband signal sources using a uniform linear array of sensors is proposed. This algorithm adopts the quasi-Newton method which is one of the most efficient gradient methods. The estimates are obtained by computing zeros of a polynomial whose coefficients can be given by solving the nonlinear optimization problem arising from the ML criterion. Simulations have indicated the following properties: the proposed method gives better estimation accuracy than MUSIC, gives almost the same accuracy as the conventional ML procedure alternating projection (AP), and requires lower computational cost than AP