Practical Considerations On The Use Of Adaptive Beamformers For Sparse Arrays With Positional Uncertainty

Abstract

Summary This paper presents the results of a study of the impacts of positional uncertainty in the array gain performance of adaptive beamforming algorithms applied to a sparse planar array. The resulting array gain degradations were computed for a range of signal-to-noise ratios and for a range of linear (pattern) and quadratic (white noise gain) constraints applied to the adaptive beamformer. The results indicate that the noise constraint is the one constraint with the largest impact on the sensitivity of adaptive beamformers to positional errors. Nonetheless, the use of multiple linear constraints can add an additional robustness that is significant in some cases. The use of a large fraction of the degrees of freedom available to the beamformer to impose linear constraints does not reduce the ability to adapt to a number (detertnined by the remaining degrees of freedom) of discrete interferers. It should also be noted that the use of linear constraints that differ significantly from the conventional beamformer's pattern can often result in large performance degradations not found when the constraints are chosen to follow the conventional beamformer's pattern. Finally, two interesting scaling relationships between the perfortnance at different frequencies and different interference powers were noted in our results.