Grate Lobes / Side Lobes Suppression for Sparse Array Design by Using Genetic Algorithms

Li, Songwen

doi:10.1109/ibica.2011.97

Cited by 7 publications

(5 citation statements)

References 2 publications

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“…To obtain the desired PSL and improve the robustness of DE for the sparse antenna array design, PSL suppression is regarded as an inequality constraint in the optimization problem as defined in (14) [18]. PSL level (PSLL) indicates the amplitude of the highest PSL outside of the desired beam pattern [11,19]. The optimization problem for the sparse antenna array design Step 2.…”

Section: Sparse Antenna Array Design Using Modementioning

confidence: 99%

Sparse Antenna Array Design for MIMO Radar Using Multiobjective Differential Evolution

Chen

Yan

Xiaolin

et al. 2016

International Journal of Antennas and Propagation

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A two-stage design approach is proposed to address the sparse antenna array design for multiple-input multiple-output radar. In the first stage, the cyclic algorithm (CA) is used to establish a covariance matrix that satisfies the beam pattern approximation for a full array. In the second stage, a sparse antenna array with a beam pattern is designed to approximate the desired beam pattern. This paper focuses on the second stage. The optimization problem for the sparse antenna array design aimed at beam pattern synthesis is formulated, where the peak side lobe (PSL) is weakly constrained by the mean squared error. To solve this optimization problem, the differential evolution (DE) algorithm with multistrategy is introduced and PSL suppression is treated as an inequality constraint. However, in doing so, a new multiobjective optimization problem is created. To address this new problem, a multiobjective differential evolution algorithm based on Pareto technique is proposed. Numerical examples are provided to demonstrate the advantages of the proposed approach over state-of-the-art methods, including DE and genetic algorithm.

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Section: Sparse Antenna Array Design Using Modementioning

confidence: 99%

Sparse Antenna Array Design for MIMO Radar Using Multiobjective Differential Evolution

Chen

Yan

Xiaolin

et al. 2016

International Journal of Antennas and Propagation

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“…However, this approach was time-consuming for large arrays. Matthew et al used a genetic algo-rithm to optimize the locations of antennas in a linear sparse array under the constraint of their physical size [12], [13]. The coefficients of elements and positions of the array were the decision variables in [14], [15] to achieve a lower side-lobe level.…”

Section: Introductionmentioning

confidence: 99%

Robust Hybrid Algorithm of PSO and SOCP for Grating Lobe Suppression and against Array Manifold Mismatch

Li¹,

Liu²,

Sun³

et al. 2018

RADIOENGINEERING

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Based on Particle Swarm Optimization (PSO)and Second-Order Cone Programming (SOCP) algorithm, this paper proposes a hybrid optimization method to suppress the grating lobes of sparse arrays and improve the robustness of array layout. With the peak side-lobe level (PSLL) as the objective function, the paper adopts the particle swarm optimization as a global optimization algorithm to optimize the elements' positions, the convex optimization as a local optimization algorithm to optimize the elements' weights. The effectiveness of the grating lobes suppression (as low as -32.13 dB) by this method is illustrated through its application to the sparse linear array when the actual steering vector is known. To enhance the robustness of the optimized array, a rebuilt robust convex optimization model is adopted in the optimization of both array excitations and layout. When the array manifold mismatch error is 1 cm, the PSLL by the robust algorithm can be compressed to -27 dB, compared to that of -24 dB by the ordinary optimization. Results of a set of representative numerical experiments show that the algorithm proposed in this paper can obtain a more robust array layout and matched elements' weight coefficients to avoid the huge degradation of the array pattern performance in the presence of array manifold mismatch errors. The good performance of pattern synthesis demonstrates the effectiveness of the proposed robust algorithm.

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“…Another objective is to reduce the energy transmitted by developing an array with fewer elements that fulfills the requirements of a full regular array through the reduction of the effective number of radiating elements. So the researchers have figured out ways to suppress peak sidelobe level using different optimization techniques such as simplex algorithm [1,2] and genetic algorithm [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. While reducing PSLL it is also desired to maintain the first null width within a specified region.…”

Section: Introductionmentioning

confidence: 99%

“…Genetic Algorithm has also been implemented to introduce nulls at the specified locations [12,13]. In papers [14][15][16][17], different approaches are discussed to achieve sparseness and thinning of antenna array using genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Synthesis of linear antenna array using genetic algorithm to reduce peak sidelobe level

Khalid

Sheikh

Shah³

et al. 2015

2015 9th International Conference on Electrical and Electronics Engineering (ELECO)

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In antenna design, it is important to optimize the amplitude weights of a linear antenna array to achieve low peak sidelobe level (PSLL). This paper deals with a linear inequality constraint on array factor to get maximum response in look direction and reduced sidelobe levels in a specified stop band region. Linear regular array as well as thinned linear array is optimized using a constrained genetic algorithm (CGA). The convergence of the optimization algorithm is also reported and its utility is shown in getting a desired antenna beam pattern with fewer radiating elements. Thinning is achieved by iteratively applying CGA which results in reducing the number of elements by 28% and overall transmitted energy by 27%, keeping a specified beam width first null (BWFN) with considerable reduction in PSLL.

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Grate Lobes / Side Lobes Suppression for Sparse Array Design by Using Genetic Algorithms

Cited by 7 publications

References 2 publications

Sparse Antenna Array Design for MIMO Radar Using Multiobjective Differential Evolution

Sparse Antenna Array Design for MIMO Radar Using Multiobjective Differential Evolution

Robust Hybrid Algorithm of PSO and SOCP for Grating Lobe Suppression and against Array Manifold Mismatch

Synthesis of linear antenna array using genetic algorithm to reduce peak sidelobe level

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