Deep learning-based sparse array design for emitter signal isolations

Kyle Juretus; Syed A. Hamza; Moeness Amin

doi:10.1117/12.3013900

10 June 2024 Deep learning-based sparse array design for emitter signal isolations

Kyle Juretus, Syed A. Hamza, Moeness Amin

Proceedings Volume 13036, Big Data VI: Learning, Analytics, and Applications; 130360H (2024) https://doi.org/10.1117/12.3013900
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

The ability of sparse arrays to significantly reduce the hardware cost and complexity over a uniform linear array (ULA) is advantageous for a variety of applications with large array sizes. While the hardware complexity is reduced, the optimum selection of active antennas for the sparse array involves iterative solutions of an optimization problem. In a dynamic environment, such a solution is deemed impractical, specifically in rapidly time-varying source and interference temporal and spatial characteristics. In essence, the computational complexity of the optimization algorithms impedes the implementation of a fast perception-action cycle, a necessity for cognitive sensing. In this regard, replacing the traditional optimization algorithms with automatic data-driven learning techniques offers a means towards real time configuration design of sparse arrays and, as such, provides prompt response to sudden changes in the operating environment. This paper examines optimum sparse array design using deep learning. We consider the case of two sources which need to be separately isolated for corresponding signal recovery and classification. One source is fixed at broadside, whereas the direction of the other changes over a 0.5◦ grid between 0◦ and 179.5◦. Multi-layer-perceptron (MLP) and convolutional neural network (CNN) architectures are both utilized on datasets varying from few to unlimited snapshots and incorporating various SNR values. The machine learning approaches demonstrate strong correlation with the optimum array configurations.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kyle Juretus, Syed A. Hamza, and Moeness Amin "Deep learning-based sparse array design for emitter signal isolations", Proc. SPIE 13036, Big Data VI: Learning, Analytics, and Applications, 130360H (10 June 2024); https://doi.org/10.1117/12.3013900

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