Space fitting the uncorrelated interference patterns in constrained adaptive antenna arrays using neural networks

An adaptive array antenna adjusts its element weightings so as to null out interference signals while maintaining a beam in the desired signal direction. This process of nulling out interferers has a constraint represented by the limited degrees of freedom. This puts limits on the number of requirements an array can achieve at a particular time. For instance, an N-element antenna array has N-1 degrees of freedom in its pattern. So if there are more interferers we need an array with a large number of elements. But here, we stop thinking about interferers as individual spot sources and we take into consideration their space distribution, their positions as long as their relative strengths, this is accomplished by constructing a general interference pattern and then studying this pattern to determine the optimal null positions. Next, the neural networks technique is used to adjust the array weights with their optimal values, which will give a general array pattern to meet the specified requirements. The previous process is fully described and illustrative examples are introduced