Robust direction-of-arrival estimation against array sensor errors using Hopfield neural network

When direction-of-arrival estimation methods based on eigenvalue expansion such as MUSIC and ESPRIT are applied to an ideal sensor array, high resolution can be attained. However, if there exist array sensor errors due to sensor position errors and degradation of the phase shifters, significant errors may arise in the estimation results. Also, in order to obtain sufficient estimation accuracy, a relatively high signal-to-noise ratio is required in the array input. In this paper, the Hopfield neural network is employed for direction-of-arrival estimation and a direction-of-arrival estimation method that is robust to array errors by virtue of using training signals is proposed. By means of computer simulation, the method is compared with that of MUSIC and its effectiveness is verified. (C) 2003 Wiley Periodicals, Inc.