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Sigmoid-enhanced robust adaptive beamforming for sensor arrays

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Abstract

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.

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Acknowledgements

We would like to express our sincere gratitude to the editors and anonymous reviewers for their valuable time. We appreciate their expertise and dedication in reviewing our manuscript, and we are grateful for their contributions to the advancement of our research.

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Author notes

  1. Yeap Kim Ho and Anand Khandare have contributed equally to this work.

Authors and Affiliations

  1. Department of CSE (Data Science), Guru Nanak Dev Engineering College, Bidar, Karnataka, 585403, India

    Veerendra Dakulagi

  2. Faculty of Engineering and Green Technology, Universiti Tunku Abdul Rahman, Kampar, Malaysia

    Kim Ho Yeap

  3. Department of AI&ML, Thakur College of Engineering and Technology, Mumbai, 400101, India

    Anand Khandare

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  1. Veerendra Dakulagi
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  2. Kim Ho Yeap
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  3. Anand Khandare
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Correspondence to Veerendra Dakulagi.

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Dakulagi, V., Yeap, K.H. & Khandare, A. Sigmoid-enhanced robust adaptive beamforming for sensor arrays. SIViP 19, 304 (2025). https://doi.org/10.1007/s11760-025-03886-2

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