SASA: Super-resolution and Ambiguity-free Sparse Array Geometry Optimization with Aperture Size Constraints for MIMO Radar

To improve the performance of multiple-input-multiple-output (MIMO) radar, various sparse arrays have been employed. However, the angular resolution of existing non-uniform arrays optimized by either combinatorial algorithms or heuristic ones is limited by the Rayleigh criterion, which is strictly r...

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Veröffentlicht in:	IEEE transactions on antennas and propagation Jg. 71; H. 6; S. 1
Hauptverfasser:	Huan, Mingsai, Liang, Junli, Wu, Yifan, Li, Yongkang, Liu, Wei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.06.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Ambiguity angular ambiguity function Angular resolution Antenna arrays Antenna radiation patterns Apertures Combinatorial analysis Direction-of-arrival estimation Heuristic algorithms MIMO radar non-convex optimization Optimization Radar antennas Radar arrays Radar imaging Sidelobes sparse array
ISSN:	0018-926X, 1558-2221
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Abstract	To improve the performance of multiple-input-multiple-output (MIMO) radar, various sparse arrays have been employed. However, the angular resolution of existing non-uniform arrays optimized by either combinatorial algorithms or heuristic ones is limited by the Rayleigh criterion, which is strictly related to the aperture size. Based on the angular ambiguity function (AAF) analysis, two new models are established in this work for directly optimizing the sidelobe level (SLL) or the main lobe width (MLW) with the constraints of aperture size and element spacing. The aforementioned designs result in non-convex and nonlinear optimization problems, and solutions are derived via the alternating direction multiplier method (ADMM). Furthermore, considering a parametric trade-off between SLL and MLW, a hybrid algorithm is proposed to search for the SLL-MLW Pareto front boundary. Finally, simulations are provided to demonstrate the high angular resolution and ambiguity-free properties of the optimized sparse arrays.
AbstractList	To improve the performance of multiple-input–multiple-output (MIMO) radar, various sparse arrays have been used. However, the angular resolution of existing nonuniform arrays optimized by either combinatorial algorithms or heuristic ones is limited by the Rayleigh criterion, which is strictly related to the aperture size. Based on the angular ambiguity function (AAF) analysis, two new models are established in this work for directly optimizing the sidelobe level (SLL) or the main lobe width (MLW) with the constraints of aperture size and element spacing. The aforementioned designs result in non-convex and nonlinear optimization problems, and solutions are derived via the alternating direction multiplier method (ADMM). Furthermore, considering a parametric tradeoff between SLL and MLW, a hybrid algorithm is proposed to search for the SLL-MLW Pareto front boundary. Finally, simulations are provided to demonstrate the high angular resolution and ambiguity-free properties of the optimized sparse arrays. To improve the performance of multiple-input-multiple-output (MIMO) radar, various sparse arrays have been employed. However, the angular resolution of existing non-uniform arrays optimized by either combinatorial algorithms or heuristic ones is limited by the Rayleigh criterion, which is strictly related to the aperture size. Based on the angular ambiguity function (AAF) analysis, two new models are established in this work for directly optimizing the sidelobe level (SLL) or the main lobe width (MLW) with the constraints of aperture size and element spacing. The aforementioned designs result in non-convex and nonlinear optimization problems, and solutions are derived via the alternating direction multiplier method (ADMM). Furthermore, considering a parametric trade-off between SLL and MLW, a hybrid algorithm is proposed to search for the SLL-MLW Pareto front boundary. Finally, simulations are provided to demonstrate the high angular resolution and ambiguity-free properties of the optimized sparse arrays.
Author	Li, Yongkang Liang, Junli Huan, Mingsai Liu, Wei Wu, Yifan
Author_xml	– sequence: 1 givenname: Mingsai orcidid: 0000-0003-3926-2748 surname: Huan fullname: Huan, Mingsai organization: School of Electronics and Information, Northwestern Polytechnical University, Xi'an, China – sequence: 2 givenname: Junli orcidid: 0000-0003-3306-2294 surname: Liang fullname: Liang, Junli organization: School of Electronics and Information, Northwestern Polytechnical University, Xi'an, China – sequence: 3 givenname: Yifan surname: Wu fullname: Wu, Yifan organization: School of Electronics and Information, Northwestern Polytechnical University, Xi'an, China – sequence: 4 givenname: Yongkang orcidid: 0000-0001-7708-9857 surname: Li fullname: Li, Yongkang organization: School of Electronics and Information, Northwestern Polytechnical University, Xi'an, China – sequence: 5 givenname: Wei orcidid: 0000-0003-2968-2888 surname: Liu fullname: Liu, Wei organization: Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield, U.K
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Cites_doi	10.1109/ISSSTA.1998.723814 10.1109/JMW.2020.3033616 10.1109/WSA.2010.5456461 10.1109/IMWS2.2009.5307896 10.1109/TAP.2020.2972331 10.1109/TAP.2010.2103550 10.1109/TAP.1986.1143830 10.1109/MSP.2020.2978507 10.1109/TSP.2019.2955828 10.1109/INMIC.2016.7840132 10.1007/b98874 10.1109/JSTSP.2009.2038964 10.1109/iccsp.2013.6577058 10.1016/B978-0-12-800806-5.00018-4 10.1016/j.sigpro.2020.107930 10.1109/MAP.2020.2988528 10.1109/TMTT.2019.2906176 10.1109/ICMIM.2018.8443558 10.1109/TAP.1968.1139138 10.1109/ACSSC.1999.831827 10.1016/j.sigpro.2021.108032 10.1109/TAP.2006.877211 10.23919/EuCAP53622.2022.9769347 10.1109/TMTT.2018.2869565 10.1049/mia2.12129 10.1109/79.526899 10.1109/ACCESS.2021.3122967 10.1109/LAWP.2016.2631889 10.1109/TAP.2022.3187513 10.1002/9780470937464 10.1007/s10957-012-0003-z 10.1109/TAP.2018.2811778 10.1109/JPROC.2015.2512389 10.1109/TMTT.2009.2017254 10.1109/MSP.2019.2903715 10.1002/9780470391488 10.1109/TAP.2020.2972643 10.1561/2200000016 10.23919/EuCAP.2017.7928056 10.1109/TAP.1973.1140564 10.1109/WSA.2011.5741909
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Snippet	To improve the performance of multiple-input-multiple-output (MIMO) radar, various sparse arrays have been employed. However, the angular resolution of... To improve the performance of multiple-input–multiple-output (MIMO) radar, various sparse arrays have been used. However, the angular resolution of existing...
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SubjectTerms	Algorithms Ambiguity angular ambiguity function Angular resolution Antenna arrays Antenna radiation patterns Apertures Combinatorial analysis Direction-of-arrival estimation Heuristic algorithms MIMO radar non-convex optimization Optimization Radar antennas Radar arrays Radar imaging Sidelobes sparse array
Title	SASA: Super-resolution and Ambiguity-free Sparse Array Geometry Optimization with Aperture Size Constraints for MIMO Radar
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