Synthesis of Sparse Arrays Based On CIGA (Convex Improved Genetic Algorithm)

Li, Jianxiong; Ren, Shuang; Guo, Chenghao

doi:10.1590/2179-10742020v19i4865

Cited by 3 publications

(4 citation statements)

References 22 publications

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“…3). To suppress grating lobes, we start with a 5×5 uniform array and set the peak side lobe level (PSLL) within the FoV as our objective function, followed by adjusting the positions of the array elements with a genetic algorithm [30] to minimize the objective function. The 10 cm aperture meets the miniaturization requirements for integration with mobile robots while maintaining an angular resolution of about 5 • based on the 3dB definition (the right beam pattern in Fig.…”

Section: B Ultrasonic Phased Array Simulationmentioning

confidence: 99%

Multi-View Reconstruction Fusing Ultrasonic Phased Array and Camera for Mobile Robots in Simulation Environment

Ren,

Hong

2024

IEEE Access

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Distance sensors are important for mobile robots to perceive surrounding environment. Typical sensors like LiDARs and depth cameras have been widely used, yet each has its limitations, such as LiDARs' relatively high cost, depth cameras' limitation to indoor use, and their poor performance in detecting transparent objects directly. On the other hand, ultrasonic phased array that integrates multiple ultrasonic sensors not only enables 3D ranging and imaging, but also provides advantages of strong environmental adaptability, being cost-effective and being able to detect transparent objects. To explore the application of in-air ultrasonic phased arrays for mobile robots, we simulate a 40 kHz 5×5 non-uniform sparse ultrasonic phased array. The simulator emulates the process of phased array transmission and reception, and utilizes algorithms such as beamforming and matched filtering to obtain depth information in three-dimensional space. Then, a multi-view indoor 3D reconstruction method fusing the ultrasonic phased array and a monocular camera is proposed, where two scanning strategies are developed to handle different scenarios. Finally, the method is validated in different Gazebo scenarios and compared with other baseline methods like LiDARs and depth cameras. The experimental results reveal the method's strong performance in terms of accuracy, consistency and completeness.

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Section: B Ultrasonic Phased Array Simulationmentioning

confidence: 99%

Multi-View Reconstruction Fusing Ultrasonic Phased Array and Camera for Mobile Robots in Simulation Environment

Ren,

Hong

2024

IEEE Access

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“…The inverse solution x * can be solved using (14), where k is the scaling factor, which is used to adjust the positions of the inverse solutions on the x-axis. n is a fine-tuning factor.…”

Section: Refraction Learningmentioning

confidence: 99%

“…Stochastic optimization methods use intelligent algorithms to find the element positions that satisfy the design requirements. Genetic algorithm (GA) [12], particle swarm optimization (PSO) algorithm [13], and simulated annealing algorithm [14] have been successfully applied to sparse sensor array design.…”

Section: Introductionmentioning

confidence: 99%

Underwater Sparse Acoustic Sensor Array Design under Spacing Constraints Based on a Global Enhancement Whale Optimization Algorithm

2022

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Sparse arrays with low cost and engineering complexity are widely applied in many fields. However, the high peak sidelobe level (PSLL) of a sparse array causes the degradation of weak target detection performance. Particularly for the large size of underwater low-frequency sensors, the design problem requires a minimum spacing constraint, which further increases the difficulty of PSLL suppression. In this paper, a novel swarm-intelligence-based approach for sparse sensor array design is proposed to reduce PSLL under spacing constrains. First, a global enhancement whale optimization algorithm (GEWOA) is introduced to improve the global search capability for optimal arrays. A three-step enhanced strategy is used to enhance the ergodicity of element positions over the aperture. In order to solve the adaptation problem for discrete array design, a position decomposition method and a V-shaped transfer function are introduced into off-grid and on-grid arrays, respectively. The effectiveness and superiority of the proposed approach is validated using experiments for designing large-scale low-frequency arrays in the marine environment. The PSLL of the off-grid array obtained by GEWOA was nearly 3.8 dB lower than that of WOA. In addition, compared with other intelligent algorithms, the on-grid array designed using GEWOA had the lowest PSLL.

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“…The authors improved the standard BBO and applied it to optimize jointly the amplitude current and element position of the CAA. A convex improved genetic algorithm (CIGA) was proposed by Li et al [ 19 ] for the beam pattern synthesis of sparse arrays. This hybrid algorithm was able to adjust the excitation and positions of the arrays to suppress the peak SLL and obtain better results as compared to some other existing algorithms.…”

Section: Introductionmentioning

confidence: 99%

Advanced Marine Predator Algorithm for Circular Antenna Array Pattern Synthesis

Owoola

Xia

Ogunjo

et al. 2022

Sensors

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The pattern synthesis of antenna arrays is a substantial factor that can enhance the effectiveness and validity of a wireless communication system. This work proposes an advanced marine predator algorithm (AMPA) to synthesize the beam patterns of a non-uniform circular antenna array (CAA). The AMPA utilizes an adaptive velocity update mechanism with a chaotic sequence parameter to improve the exploration and exploitation capability of the algorithm. The MPA structure is simplified and upgraded to overcome being stuck in the local optimum. The AMPA is employed for the joint optimization of amplitude current and inter-element spacing to suppress the peak sidelobe level (SLL) of 8-element, 10-element, 12-element, and 18-element CAAs, taking into consideration the mutual coupling effects. The results show that it attains better performances in relation to SLL suppression and convergence rate, in comparison with some other algorithms for the optimization case.

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Synthesis of Sparse Arrays Based On CIGA (Convex Improved Genetic Algorithm)

Cited by 3 publications

References 22 publications

Multi-View Reconstruction Fusing Ultrasonic Phased Array and Camera for Mobile Robots in Simulation Environment

Multi-View Reconstruction Fusing Ultrasonic Phased Array and Camera for Mobile Robots in Simulation Environment

Underwater Sparse Acoustic Sensor Array Design under Spacing Constraints Based on a Global Enhancement Whale Optimization Algorithm

Advanced Marine Predator Algorithm for Circular Antenna Array Pattern Synthesis

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