Study of a new antenna system for multiple suppression

A beamformer is a processor used in conjunction with an array of sensors to provide a versatile form of spatial filtering. The sensor array collects spatial samples of propagating wave fields, which are processed by the beamformer. The objective is to estimate the signal arriving from a desired direction in the presence of noise and interfering signals. Generally, the beamformer design is severely hampered by noise and surrounding interference environment. Several beamformer algorithms were proposed in the literature in the fields of radar, sonar and communication systems. One of the simple array systems, widely employed for interference suppression, was introduced by Frost (1972). The weight adjustment in the Frost array is computed by the popular adaptive least mean square (LMS) algorithm. The LMS algorithm is a member of the stochastic gradient class of the algorithms, climbing down the mean-square error contour in the direction of the negative gradient. It is well known that the convergence performance of this algorithm is highly related to the eigenvalue spread of the autocorrelation matrix of the array input data. As a result, the Frost array also suffers from the same drawback. In this research, we are concerned with the development of suitable algorithms to improve the performance of the Frost array