A precise ultra-wideband ranging method using pre-corrected strategy and particle swarm optimization algorithm

Li, Di; Wang, Xianglong; Chen, Dongdong; Zhang, Qidong; Yang, Yanchao

doi:10.1016/j.measurement.2022.110966

Cited by 17 publications

(8 citation statements)

References 40 publications

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“…γ and γ are random numbers from zero to one. Formula (17) indicates that the future position of a particle is the result of the addition of the previous position of the particle to the next velocity of motion [16].…”

Section: Standard Particle Swarm Optimizationmentioning

confidence: 99%

“…First, the homogenized Logistic sequence [v D ] is obtained by Formula (18), and then the initial position of the particles is obtained by using the resulting sequence in Formula (19). When the initial position of each particle is obtained by the particle population, the initial particle population is substituted into the fitness function, based on which the uniform Logistic mapping changes mentioned above are made, and the search interval is iterated and searched [17]. In this process, a particle in the original particle needs to be replaced by a mutant particle to ensure the efficiency of optimization.…”

Section: Particle Swarm Algorithm For Homogenizing Logistic Mapsmentioning

confidence: 99%

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Particle Swarm Algorithm Based on Homogenized Logistic Mapping and Its Application in Antenna Parameter Optimization

Nan,

Liyong

2022

IJICS

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Aiming at the problems of computing complexity, long time-consuming and low accuracy in the process of antenna optimization design, a particle swarm optimization algorithm based on chaotic ergodic search is proposed in this paper. In order to improve the rationality of the selection of the initial population of the traditional particle swarm optimization algorithm and enhance the diversity of particles in the iterative process of the algorithm, this algorithm introduces a uniform Logistic chaotic map into the traditional particle swarm optimization algorithm, so as to improve the convergence speed and optimization performance of the particle swarm optimization more effectively. In this paper, the Logistic map is selected in the chaotic map, and the analysis shows that although it has good long-term periodicity and initial value sensitivity, the data does not obey the uniform distribution, which makes the omission range when the sequence value is small, which reduces the chaos. The efficiency of traversal, in view of this shortcoming, this paper proposes a method of homogenizing Logistic, and deduces and analyzes it. It is concluded that this homogenized Logistic method has better randomness and can better reflect the characteristics of uniform distribution of data. Further, based on the optimization algorithm, the rectangular microstrip antennas with two feeding modes are optimized, and the antenna size obtained by empirical formula is modeled and analyzed in HFSS software. The research results show that the optimization algorithm in this paper can bring faster convergence speed and more accurate optimization results for antenna optimization.

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Section: Standard Particle Swarm Optimizationmentioning

confidence: 99%

Section: Particle Swarm Algorithm For Homogenizing Logistic Mapsmentioning

confidence: 99%

Particle Swarm Algorithm Based on Homogenized Logistic Mapping and Its Application in Antenna Parameter Optimization

Nan,

Liyong

2022

IJICS

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“…Chen et al [30] developed an optimization design strategy for CTSV based on the artificial neural network model to improve the design efficiency of 3D integrated system. Moreover, the PSO with linear decreasing inertia weight (PSO-LDIW) algorithm has been utilized to improve the positioning accuracy of ultra-wideband system [31,32] and optimize the design parameters of electronic components [33][34][35]. Thus, the PSO-LDIW algorithm can be applied in the multi-physics coupling optimization design of CTSV for high performance Chiplet-based microsystem.…”

Section: Introductionmentioning

confidence: 99%

Electro-Thermal and Thermal-Stress Coupling Optimization Design for Coaxial Through Silicon Via

Wang,

Yang,

Chen

et al. 2024

Preprint

Self Cite

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In this research, based on the back propagation neural network (BPNN) model and particle swarm optimization with linear decreasing inertia weight (PSO-LDIW) algorithm, an electro-thermal and thermal-stress fields coupling optimization design method for coaxial through silicon via (CTSV) is provided. The irregular and complex relationship between the parameters (height of CTSV, radius, thickness of SiO2, BCB and coaxial annular) and indexes is investigated by COMSOL Multiphysics and HFSS software. According to the simulation data of COMSOL and HFSS, the BPNN models are used to express the corresponding relationship between the parameters and indexes of CTSV. According to the desired indexes of CTSV, the multi-objective optimization function is formulated. Then, the PSO-LDIW algorithm is applied to optimize the parameters of CTSV. Finally, the simulation experiment is used to verify the effectiveness of the optimization design strategy. The simulated indexes (-40.28 dB, 366.31 K, 115.21, 74.14 and 23.16 MPa) well consent to the desired ones (-40 dB, 360 K, 110, 70 and 25 MPa), which indicates that the parameters of CTSV can effectively optimized by the developed design method to control the indexes. Therefore, the developed electro-thermal and thermal-stress fields coupling optimization design method can effectively design CTSV for manufacturing high-performance Chiplet-based microsystem.

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“…Recently, the artificial neural network (ANN), has been widely used in the model predicted control [15][16][17], image processing [18][19][20] and optimization algorithms [21][22][23]. In addition, the particle swarm optimization (PSO) algorithm has been utilized in the topology optimization for compliant mechanisms [24,25], the optimal design parameters of electronic components [26] and the optimal parameters of the filtering algorithm [27,28]. Therefore, the PSO algorithm can be adopted to obtain the optimal design parameters of coaxial TSV based on the thermal-stress coupling physics field for high-efficiency thermal-stress management.…”

Section: Introductionmentioning

confidence: 99%

Thermal-Stress Coupling Optimization for Coaxial through Silicon Via

et al. 2023

Self Cite

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In this paper, a thermal-stress coupling optimization strategy for coaxial through silicon via (TSV) is developed based on the finite element method (FEM), artificial neural network (ANN) model and particle swarm optimization (PSO) algorithm. In order to analyze the effect of design parameters on the thermal-stress distribution of coaxial TSV, the FEM simulations of coaxial TSV are conducted by COMSOL Multiphysics. The structure of coaxial TSV is symmetric. The mapping relationships between the design parameters and performance indexes are described by ANN models based on the simulation data of FEM. In addition, the multi-objective optimization function is formulated based on the desired performance indexes, and then the design parameters are optimized by the modified PSO algorithm. Based on the optimized design parameters, the effectiveness of the developed method is validated by FEM simulations. The simulated performance indexes agree well with the desired ones, which implies that the design parameters of coaxial TSV can be optimized to control the thermal-stress distribution. Therefore, the thermal-stress coupling optimization of coaxial TSV can achieve thermal-stress management to improve its reliability.

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A precise ultra-wideband ranging method using pre-corrected strategy and particle swarm optimization algorithm

Cited by 17 publications

References 40 publications

Particle Swarm Algorithm Based on Homogenized Logistic Mapping and Its Application in Antenna Parameter Optimization

Particle Swarm Algorithm Based on Homogenized Logistic Mapping and Its Application in Antenna Parameter Optimization

Electro-Thermal and Thermal-Stress Coupling Optimization Design for Coaxial Through Silicon Via

Thermal-Stress Coupling Optimization for Coaxial through Silicon Via

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