Cross Ambiguity Function Shaping of Cognitive MIMO Radar: A Synergistic Approach to Antenna Placement and Waveform Design
The Ambiguity Function (AF) is a crucial tool in characterizing the range-angle response of a Multiple-Input-Multiple-Output (MIMO) radar system, which is intricately influenced by the transmit waveforms, receiving filters and also antenna configurations. Notably, the role of antenna configurations...
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| Published in | IEEE transactions on geoscience and remote sensing Vol. 62; p. 1 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0196-2892 1558-0644 |
| DOI | 10.1109/TGRS.2024.3415428 |
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| Abstract | The Ambiguity Function (AF) is a crucial tool in characterizing the range-angle response of a Multiple-Input-Multiple-Output (MIMO) radar system, which is intricately influenced by the transmit waveforms, receiving filters and also antenna configurations. Notably, the role of antenna configurations is less explored compared to the well-studied areas of waveforms and filters. In this paper, we incorporate antenna positions as an additional design parameter alongside waveforms and filters to optimize the AF in a specific range-angle bin. We employ the Mainlobe-to-Integrated-Sidelobe-Level-Ratio (MISLR) as a quantitative metric to assess performance. The resulting optimization problem is inherently non-convex, encompassing binary and unimodular constraints. To address this challenge, we reformulate the problem, enabling an alternating optimization approach for antenna positions and waveforms. Each iteration involves solving a sequence of quadratic constrained quadratic programming problems for the binarily constrained antenna position optimization and updating the waveforms iteratively via an analytical expression. Our simulation results validate the effectiveness of the proposed method as it achieves higher MISLR with the same number of antennas compared to conventional approaches. Moreover, the optimized antenna configurations notably enhance the balance between angular ambiguity and resolution. |
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| AbstractList | The Ambiguity Function (AF) is a crucial tool in characterizing the range-angle response of a Multiple-Input-Multiple-Output (MIMO) radar system, which is intricately influenced by the transmit waveforms, receiving filters and also antenna configurations. Notably, the role of antenna configurations is less explored compared to the well-studied areas of waveforms and filters. In this paper, we incorporate antenna positions as an additional design parameter alongside waveforms and filters to optimize the AF in a specific range-angle bin. We employ the Mainlobe-to-Integrated-Sidelobe-Level-Ratio (MISLR) as a quantitative metric to assess performance. The resulting optimization problem is inherently non-convex, encompassing binary and unimodular constraints. To address this challenge, we reformulate the problem, enabling an alternating optimization approach for antenna positions and waveforms. Each iteration involves solving a sequence of quadratic constrained quadratic programming problems for the binarily constrained antenna position optimization and updating the waveforms iteratively via an analytical expression. Our simulation results validate the effectiveness of the proposed method as it achieves higher MISLR with the same number of antennas compared to conventional approaches. Moreover, the optimized antenna configurations notably enhance the balance between angular ambiguity and resolution. The ambiguity function (AF) is a crucial tool in characterizing the range-angle response of a multiple-input multiple-output (MIMO) radar system, which is intricately influenced by the transmit waveforms, receiving filters and also antenna configurations. Notably, the role of antenna configurations is less explored compared to the well-studied areas of waveforms and filters. In this article, we incorporate antenna positions as an additional design parameter alongside waveforms and filters to optimize the AF in a specific range-angle bin. We employ the mainlobe-to-integrated-sidelobe-level-ratio (MISLR) as a quantitative metric to assess the performance. The resulting optimization problem is inherently nonconvex, encompassing binary and unimodular constraints. To address this challenge, we reformulate the problem, enabling an alternating optimization approach for antenna positions and waveforms. Each iteration involves solving a sequence of quadratic constrained quadratic programming problems for the binarily constrained antenna position optimization and updating the waveforms iteratively via an analytical expression. Our simulation results validate the effectiveness of the proposed method as it achieves higher MISLR with the same number of antennas compared to conventional approaches. Moreover, the optimized antenna configurations notably enhance the balance between angular ambiguity and resolution. |
| Author | Wu, Linlong Huang, Xiaotao Liu, Wei Xie, Zhuang Fan, Chongyi Zhu, Jiahua |
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| Cites_doi | 10.1109/RADAR.2018.8378710 10.1109/LAWP.2012.2227232 10.1109/TSP.2016.2569431 10.1109/TVT.2019.2951399 10.1109/TAES.2018.2870459 10.1109/TGRS.2022.3232528 10.1109/TSP.2017.2787115 10.1017/CBO9780511804441 10.1109/TAES.2020.3037409 10.1109/MAES.2016.160071 10.1109/TSP.2019.2952052 10.1109/TSP.2009.2012562 10.1109/VTC2021-Fall52928.2021.9625517 10.1109/JSEN.2020.2977686 10.1109/TSP.2023.3282705 10.1109/TVT.2023.3273206 10.1109/TSP.2023.3315448 10.1109/TAES.2023.3260814 10.1109/TSP.2022.3176953 10.1109/LSP.2017.2700396 10.1109/TSP.2016.2543207 10.1109/LSP.2014.2370033 10.1016/j.sigpro.2023.109075 10.1109/TSP.2024.3355768 10.1049/iet-rsn.2018.5438 10.1109/TSP.2016.2621723 10.1109/LSP.2014.2298497 10.1017/CBO9781139020411 10.1109/LCOMM.2023.3257739 10.1109/TGRS.2022.3217577 10.1109/MCOM.2004.1341263 10.1109/TVT.2021.3133596 10.1109/TAES.2022.3163120 10.1109/SSP49050.2021.9513770 10.1109/LSP.2013.2289325 10.1109/IEEECONF44664.2019.9048709 10.1007/s11432-022-3651-8 10.1109/TSP.2022.3181346 10.1002/0471221104 10.1109/TSP.2015.2425808 10.1109/TSP.2017.2780052 10.1109/TSP.2017.2723354 10.1109/TSP.2015.2510982 10.1109/TSP.2017.2760279 10.1016/j.sigpro.2021.107985 10.1109/TAES.2021.3090900 10.1109/TSP.2018.2887186 10.1109/TAES.2013.6404093 10.1109/TGRS.2021.3065335 10.1109/TSP.2023.3244096 10.1109/TGRS.2020.3008320 10.1109/TAES.2023.3299437 10.1109/TSP.2024.3371292 10.1109/TSP.2023.3244672 10.1109/JSTSP.2007.897058 10.1109/TVT.2020.3002773 10.1109/TSP.2020.2979602 10.1109/TAP.2023.3262157 10.1109/TAES.2022.3150619 10.1109/TRS.2023.3290846 10.1109/TSP.2018.2883035 10.1109/TGRS.2021.3131590 |
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| Snippet | The Ambiguity Function (AF) is a crucial tool in characterizing the range-angle response of a Multiple-Input-Multiple-Output (MIMO) radar system, which is... The ambiguity function (AF) is a crucial tool in characterizing the range-angle response of a multiple-input multiple-output (MIMO) radar system, which is... |
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| SubjectTerms | Ambiguity Ambiguity Function (AF) Antenna arrays antenna positions Antennas Configurations Constraints Design parameters Filters Majorization-Minimization (MM) MIMO communication MIMO radar Optimization Performance assessment Quadratic programming Radar Radar antennas Radar equipment Sidelobes Vectors waveform design Waveforms |
| Title | Cross Ambiguity Function Shaping of Cognitive MIMO Radar: A Synergistic Approach to Antenna Placement and Waveform Design |
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