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sparse array design via Integer Linear Programming

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 4812-4826页

作者： Zhuang, Yangjingzhi Zhang, Xuejing He, Zishu Greco, Maria Sabrina Gini, Fulvio Univ Elect Sci & Technol China Dept Informat & Commun Engn Chengdu 611731 Peoples R China Univ Pisa Dept Informat Engn I-56122 Pisa Italy

In this paper, a design framework based on integer linear programming is proposed for optimizing sparse array structures. We resort to binary vectors to formulate the design problem for non-redundant arrays (NRA) and minimum-redundant arrays (MRA). The flexibility of the proposed framework allows for dynamic adjustment of constraints to meet various applicative requirements, e.g., to achieve desired array apertures and mitigate mutual coupling effects. The proposed framework is also extended to the design of high-order arrays associated by exploiting high-order cumulants. The effectiveness of the proposed sparse array design framework is investigated through extensive numerical analysis. A comparative analysis with closed-form solutions and integer linear programming-based array design methods confirms the superiority of the proposed design framework in terms of number of degrees of freedom (DOF) and direction of arrival (DOA) estimation accuracy.

关键词： Sensor arrays Antenna arrays Mutual coupling Vectors Sensors Redundancy Apertures Non-redundant array minimum-redundant array sparse array design integer linear programming

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sparse array design for Dual-Function Radar-Communications System

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IEEE COMMUNICATIONS LETTERS 2023年第5期27卷 1412-1416页

作者： Huang, Huiping Wu, Linlong Shankar, Bhavani Zoubir, Abdelhak M. M. Tech Univ Darmstadt Dept Elect Engn & Informat Technol D-64283 Darmstadt Germany Chalmers Univ Technol Dept Elect Engn S-41296 Gothenburg Sweden Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust SNT L-4365 Luxembourg Luxembourg Tech Univ Darmstadt Dept Elect Engn & Informat Technol D-64283 Darmstadt Germany

The problem of sparse array design for dual-function radar-communications is investigated. Our goal is to design a sparse array which can simultaneously shape desired beam responses and serve multiple downlink users with the required signal-to-interference-plus-noise ratio levels. Besides, we also take into account the limitation of the radiated power by each antenna. The problem is formulated as a quadratically constrained quadratic program with a joint-sparsity-promoting regularization, which is NP-hard. The resulting problem is solved by the consensus alternating direction method of multipliers, which enjoys parallel implementation. Numerical simulations exhibit the effectiveness and superiority of the proposed method which leads to a more power-efficient solution.

关键词： Signal to noise ratio Manganese Antennas Interference Radio frequency Quality of service Simulation Alternating direction method of multipliers dual-funtion radar-communications sparse array design

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High-Order Cumulants Based sparse array design Via Fractal Geometries-Part I: Structures and DOFs

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2023年 71卷 327-342页

作者： Yang, Zixiang Shen, Qing Liu, Wei Eldar, Yonina C. C. Cui, Wei Beijing Inst Technol Sch Informationand Elect Beijing 100081 Peoples R China Univ Sheffield Dept Elect & Elect Engn Sheffield S1 3JD England Weizmann Inst Sci Fac Math & Comp Sci IL-7610001 Rehovot Israel

structures based on the high-order difference co-array concept provide a large number of degrees of freedom, but are typically difficult to design under multiple optimality criteria. In this paper, we present a joint across-order (across different cumulant order q) and inner-order (within the same cumulant order q) fractal framework to form a fractal array based on the 2qth-order difference co-array (2qth-O-Fractal) by recursively using a simple generator. We show that multiple properties of interest, including large consecutive difference co-array, closed-form sensor positions, hole-free difference co-array, robustness to sensor failures, and resilience to mutual coupling, are inherited from the generator under appropriate conditions. Part I of the work focuses on array structures with a large uniform or hole-free (higher-order) difference co-array. First, we show that for an array of size N, O(N-2q) consecutive co-array lags can be provided by optimizing the generator. In addition, the generated structure outperforms existing structures in terms of the number of consecutive lags offered. Then, proof is provided that under given requirements on the generator, the hole-free property is inherited for q = 2, and O(N-4) hole-free fourth-order difference co-array lags can be achieved by the proposed framework, which is larger than those of existing structures. Simulation results verify the superiority of the proposed framework in terms of estimation accuracy and resolution capability. Part II of this work focuses on the properties of array robustness and mutual coupling.

关键词： sparse array design difference co-array high-order cumulants fractal geometry direction of arrival estimation

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Joint transmit filter and sparse array design with transmit subaperturing FDA-MIMO radar

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IET SIGNAL PROCESSING 2022年第2期16卷 132-140页

作者： Ding, Zihang Xie, Junwei Air Force Engn Univ Air & Missile Def Coll Xian 710038 Shaanxi Peoples R China

Increasingly complex electromagnetic environment makes some troubles for a radar system. Improving the performance of the radar detection and suppressing the interferences are important challenges. In this study, joint transmit filters and sparse array design enabling the maximum signal to interference plus the noise ratio (SINR) beamforming at the transmit subaperturing frequency diverse array multiple-input multiple-output (TS-FDA-MIMO) radar is examined. However, the optimisation problem is non-convex. A cycle iteration algorithm is proposed to obtain the optimal transmit filters and sparse array. The total problem is divided into two sub-problems. The first optimisation sub-problem is the sparse design and the Successive Convex Approximation (SCA) with two-dimensional reweighted mixed l1, 2-norm method is introduced to solve the sub-problem. In addition, the transmit filters design is the second optimisation problem. The sub-problem is changed to the semi-definite relaxation (SDR) problem and the Gaussian random method is utilised to obtain the optimal transmit filters. Through the cycle iteration of the two sub-problems, the optimal solutions are obtained. Finally, the simulation results verified the effectiveness of the proposed method.

关键词： sparse design transmit subaperturing frequency diverse array multiple-input multiple-output radar semidefinite relaxation problem TS-FDA-MIMO complex electromagnetic environment optimisation problem MIMO radar approximation theory total problem 2-norm method filtering theory convex programming iterative methods radar signal processing optimisation sub-problem optimal transmit filters transmit subaperturing FDA-MIMO radar Gaussian processes sparse array design radar detection array signal processing joint transmit filter radar system

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LOW MUTUAL COUPLING sparse array design USING ULA FITTING

LOW MUTUAL COUPLING SPARSE ARRAY DESIGN USING ULA FITTING

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Shi, Wanlu Li, Yingsong Vorobyov, Sergiy A. Harbin Engn Univ Harbin Peoples R China Aalto Univ Espoo Finland

ISBN: (纸本)9781728176055

In this paper, a general sparse array (SA) design principle, called uniform linear array (ULA) fitting, is proposed. It uses concatenation of sub-ULAs to design SAs with feasible difference coarrays (DCAs). Motivation for the ULA fitting is that the nested array and coprime array are in fact the examples of concatenations of two sub-ULAs which provide good properties. The polynomial model is utilized to investigate the case when an SA is composed of multiple sub-ULAs. An example of SA designed via ULA fitting is presented, and it attests that the ULA fitting enables to design SAs with closed-form expressions, low coupling leakage and long consecutive DCA.

关键词： sparse array design polynomial model coupling leakage DOA estimation difference coarray

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Deep Learning Based sparse array design for Emitter Signal Isolations 6

Deep Learning Based Sparse Array Design for Emitter Signal I...

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Conference on Big Data VI - Learning, Analytics, and Applications

作者： Juretus, Kyle Hamza, Syed Ali Amin, Moeness Villanova Univ ECE Dept Villanova PA 19085 USA Widener Univ Sch Engn Chester PA 19013 USA Villanova Univ Ctr Adv Commun Villanova PA 19085 USA

ISBN: (纸本)9781510673915;9781510673908

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 degrees grid between 0 degrees and 179.5 degrees. 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.

关键词： sparse array design Beamforming Machine Learning

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sparse array Beamformer design via ADMM

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2023年 71卷 3357-3372页

作者： Huang, Huiping So, Hing Cheung Zoubir, Abdelhak M. Tech Univ Darmstadt Dept Elect Engn & Informat Technol D-64283 Darmstadt Germany Chalmers Univ Technol Dept Elect Engn S-41296 Gothenburg Sweden Tech Univ Darmstadt Dept Elect Engn & Informat Technol D-64283 Darmstadt Germany City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

In this paper, we devise a sparse array design algorithm for adaptive beamforming. Our strategy is based on finding a sparse beamformer weight to maximize the output signal-to-interference-plus-noise ratio (SINR). The proposed method utilizes the alternating direction method of multipliers (ADMM), and admits closed-form solutions at each ADMM iteration. The algorithm convergence properties are analyzed by showing the monotonicity and boundedness of the augmented Lagrangian function. In addition, we prove that the proposed algorithm converges to the set of Karush-Kuhn-Tucker stationary points. Numerical results exhibit its excellent performance, which is comparable to that of the exhaustive search approach, slightly better than those of the state-of-the-art solvers, including the semidefinite relaxation (SDR), its variant (SDR-V), and the successive convex approximation (SCA) approaches, and significantly outperforms several other sparse array design strategies, in terms of output SINR. Moreover, the proposed ADMM algorithm outperforms the SDR, SDR-V, and SCA methods, in terms of computational complexity.

关键词： Adaptive beamforming ADMM semidefinite relaxation sparse array design successive convex approximation output SINR

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sparse array Beamformer design via ADMM 12

Sparse Array Beamformer Design via ADMM

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IEEE 12th Sensor array and Multichannel Signal Processing Workshop (SAM)

作者： Huang, Huiping So, Hing Cheung Zoubir, Abdelhak M. Tech Univ Darmstadt Darmstadt Germany City Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781665406338

In this paper, we devise a sparse array design algorithm for adaptive beamforming. Our strategy is based on finding a sparse beamformer weight to maximize the output signal-tointerference-plus-noise ratio (SINR). The proposed method uses the alternating direction method of multipliers (ADMM), and admits closed-form solutions at each ADMM iteration. Numerical results exhibit excellent performance of the proposed method, which is comparable to that of the exhaustive search approach.

关键词： Adaptive beamforming ADMM sparse array design output SINR

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Stretching Method Using Chebyshev Polynomial for Linear sparse array Antenna design 21

Stretching Method Using Chebyshev Polynomial for Linear Spar...

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21st International Symposium on Antennas and Propagation (ISAP)

作者： Sandi, Efri Zulkifli, Fitri Yuli Basari Rahardjo, Eko Tjipto Univ Indonesia Antenna Propagat & Microwave Res Grp AMRG Dept Elect Engn Fac Engn Kampus Baru UI Depok Depok 16424 Indonesia

ISBN: (纸本)9784885523137

In this paper, a novel non-uniform stretching method of linear sparse array antenna design is proposed. This method is developed by applying new approach of inter-element spacing stretching strategy based on Chebyshev polynomial technique. The performance of proposed method is compared to full array antenna configuration with half-wavelength spacing. The simulation result shown the stretching-Chebyshev method has improved sidelobes level (SLL) compared to full array configuration with significant reduced number of array elements.

关键词： Stretching Method sparse array design Non-uniform elements spacing Chebyshev Polynomial

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Stretching Method Using Chebyshev Polynomial for Linear sparse array Antenna design

Stretching Method Using Chebyshev Polynomial for Linear Spar...

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International Symposium on Antennas and Propagation

作者： Efri Sandi Fitri Yuli Zulkifli Basari Eko Tjipto Rahardjo Antenna Propagation and Microwave Research Group (AMRG) Department of Electrical Engineering Faculty of Engineering Universitas Indonesia

ISBN: (纸本)9781467379601

关键词： Stretching Method sparse array design Non-uniform elements spacing Chebyshev Polynomial

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