Version 1
: Received: 25 July 2024 / Approved: 26 July 2024 / Online: 26 July 2024 (06:49:32 CEST)
How to cite:
Tanyer, S. G.; Dent, P.; Ali, M.; Davis, C.; Rajagopal, S.; Driessen, P. F. Multi-objective Design and Optimization of Hardware Friendly Grid-Based Sparse MIMO Arrays. Preprints2024, 2024072140. https://doi.org/10.20944/preprints202407.2140.v1
Tanyer, S. G.; Dent, P.; Ali, M.; Davis, C.; Rajagopal, S.; Driessen, P. F. Multi-objective Design and Optimization of Hardware Friendly Grid-Based Sparse MIMO Arrays. Preprints 2024, 2024072140. https://doi.org/10.20944/preprints202407.2140.v1
Tanyer, S. G.; Dent, P.; Ali, M.; Davis, C.; Rajagopal, S.; Driessen, P. F. Multi-objective Design and Optimization of Hardware Friendly Grid-Based Sparse MIMO Arrays. Preprints2024, 2024072140. https://doi.org/10.20944/preprints202407.2140.v1
APA Style
Tanyer, S. G., Dent, P., Ali, M., Davis, C., Rajagopal, S., & Driessen, P. F. (2024). Multi-objective Design and Optimization of Hardware Friendly Grid-Based Sparse MIMO Arrays. Preprints. https://doi.org/10.20944/preprints202407.2140.v1
Chicago/Turabian Style
Tanyer, S. G., SenthinelKumar Rajagopal and Peter F. Driessen. 2024 "Multi-objective Design and Optimization of Hardware Friendly Grid-Based Sparse MIMO Arrays" Preprints. https://doi.org/10.20944/preprints202407.2140.v1
Abstract
A comprehensive design framework is proposed for optimizing sparse MIMO (multiple-input, multiple-output) arrays to enhance multi-target detection. The framework emphasizes efficient utilization of antenna resources, including strategies for minimizing inter-element mutual coupling and exploring alternative grid-based sparse array (GSA) configurations by efficiently separating interacting elements. Alternative strategies are explored to enhance angular beamforming metrics, including beamwidth (BW), peak-to-sidelobe ratio (PSLR), and grating lobe limited field of view. Additionally, a set of performance metrics is introduced to evaluate virtual aperture effectiveness and beamwidth loss factors. The study employs the desirability function and explores optimization strategies for the partial sharing of antenna elements in multi-mode radar applications. A novel machine learning initialization approach is introduced for rapid convergence. Observations include the potential for peak-to-sidelobe ratio (PSLR) reduction in dense arrays and insights into GSA feasibility and performance compared to uniform arrays. The study validates the efficacy of the proposed framework through simulated and measured results. The study underscores the importance of effective sparse array processing in multi-target scenarios and highlights the advantages of the proposed design framework, offering clarity on sparse array efficiency and applicability in processing hardware providing clarity on its advantages over uniform and nonuniform arrays.
Keywords
grid-based sparse MIMO arrays; array design and optimization; mitigation of mutual coupling; adaptive desirability function; grating-lobe free arrays; side lobe reduction; machine learning
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
