Sigmoid-enhanced robust adaptive beamforming for sensor arrays

In this paper, a novel robust beamforming method for sensor arrays is presented. Unlike conventional methods, this novel technique employs a normalised sigmoid function-based adaptation to dynamically adjust the step size. Due to this adaptive mechanism, the algorithm can respond to changes in the signal environment without manually adjusting the parameters, which can be difficult and time-consuming. The startling efficiency of our suggested method while maintaining a reasonably low computational cost is one of its outstanding benefits. This is made possible by the straightforward implementation and intrinsic parallelizability of the sigmoid function-based adaptation, which further reduces computing complexity. Furthermore, the proposed sigmoid function-based algorithm creates a potent technique that improves overall performance and adaptability. The proposed algorithm is superior to existing approaches in terms of convergence speed, tracking ability, and interference suppression, as shown by rigorous simulations and in-depth comparisons with them. The proposed method offers significant promise for a variety of real-world scenarios by providing a reliable and effective remedy to the drawbacks of conventional beamforming algorithms, thereby advancing the science of sensor array beamforming.