Beam steered adaptive arrays based on subspace decomposition approach

Abstract

The main objectives of this dissertation work are to develop the algorithms that are robust to steering errors and provide high interferencenulling capabilities, thereby improving the performances of beam steered adaptive arrays. When a beam-steered adaptive array is used as a tool to estimate the desired signal of the known direction in the presence of unwanted signals, it suffers significant performance degradation when there exist steering errors. The steering errors may be caused by the look-direction error or the random steering errors. The random steering errors are caused mainly by the mismatches between the actual and assumed values of the element positions, receiver sensitivities and/or antenna gains. In array signal processing, the development of a high resolution adaptive array is also very important, since the interferences due to their coherent properties are more destructive than the background noise even though the powers of the interferences are smaller than the noise power. The interferences become more destructive when they are correlated with the desired signal. In this dissertation work, we suggest several schemes to solve these problems. First, we present a signal subspace method that is robust to steering errors due to the random steering errors as well as the look-direction error. In the proposed signal subspace method, the weight vector is calculated by using the eigenvectors lying the signal subspace. This procedure reduces the degrees of freedom (DOF) of the array weight control loop and thus makes the array robust to steering errors. Since the signal subspace completely represents the subspace spanned by the external signals, the reduction of DOF in this way does not lose any information needed and preserves the optimum characteristic of the array in the absence of steering errors. Also, this scheme does not reduce the interference-nulling capability of the array. Thus, there is no tradeoff between the array performance and the robust...