Study of Constellation Design of Pseudolites Based on Improved Adaptive Genetic Algorithm

Song, Jiancai; Hou, Chunping; Xue, Guixiang

doi:10.12720/jcm.11.9.879-885

Cited by 13 publications

(6 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Assuming that an IPCB composed of n p IPBs is deployed above the service area initially and that n u users are selected as samples to assess IPCB geometry performance. Then, the pseudo-range equation from the j-th IPB to the i-th user at time t can be expressed by Equation (1) [31][32][33].…”

Section: Performance Indicator Of Ipcb Geometry Configurationmentioning

confidence: 99%

IPCB: Intelligent Pseudolite Constellation Based on High-Altitude Balloons

Qu,

Wang,

Pan

et al. 2024

Electronics

View full text Add to dashboard Cite

IPCBs (Intelligent Pseudolite Constellations based on high-altitude balloons) are a novel type of air-based pseudolite application with many advantages. Compared with ground-based pseudolites and traditional air-based pseudolites, IPCBs have a wider coverage and a lower energy requirement. Compared with LEO satellite constellations, IPCBs have a stronger signal, a lower cost, and a shorter deployment period. These merits give promising potential to IPCBs. In IPCB applications, one of the key factors is geometry configuration, which is deeply influenced by the balloon’s unique features. The basic idea of this paper is to pursue a strategy to improve IPCB geometry performance by using diverse winds at different altitudes and balloons’ capability of altering flight altitude intelligently. Starting with a brief introduction to IPCBs, this paper defines an indicator to assess IPCB geometry performance, an approach to adjust IPCB geometry configuration and an IPCB geometry configuration planning algorithm. Next, a series of simulations are implemented with an IPCB composed of six pseudolites in winds with/without a quasi-zero wind layer. Some IPCB geometry configurations are analyzed, and their geometry performances are compared. Simulation results show the effectiveness of the proposed algorithm and the influence of the quasi-zero wind layer on IPCB performance.

show abstract

Section: Performance Indicator Of Ipcb Geometry Configurationmentioning

confidence: 99%

IPCB: Intelligent Pseudolite Constellation Based on High-Altitude Balloons

Qu,

Wang,

Pan

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…Conversely, for individuals with poor fitness, larger Pcs and Pms are used to enhance the global search capabilities of the algorithm. Building upon this concept, various adaptive genetic algorithms have been developed [16][17][18][19]. Yan et al proposed a bilinear adjustment model for crossover and mutation probabilities [18].…”

Section: Adaptive Genetic Algorithmmentioning

confidence: 99%

Parameter Optimization of Friction Pendulum Bearings Based on the Adaptive Genetic Algorithm Considering the Overall Evolutionary Status

Yin,

Ma,

Jia

et al. 2024

Buildings

View full text Add to dashboard Cite

Improper design of friction pendulum bearings can lead to poor seismic reduction performance and may result in the failure of local vulnerable components. And the parameter design of friction pendulum bearings mainly relies on experience and verification calculations at present. This paper proposes an adaptive genetic algorithm considering the overall evolution state of the population, adjusting crossover and mutation probabilities adaptively based on individual fitness and population diversity. Compared to traditional algorithms, it exhibits better global search capabilities and convergence efficiency. Combining the improved genetic algorithm with finite element models, a parameter optimization method is proposed. The parameters of friction pendulum bearings are optimized. In response to the situation in this paper, the optimal friction coefficient of the friction pendulum bearing is determined to be 0.01 and the optimal equivalent radius is 3.3 m. This can provide a reference for the design of seismic isolation devices.

show abstract

“…There are two categories of AGA according to the different adjustment methods for P c and P m : probabilistic linear adjustment AGA [16][17][18] and probabilistic nonlinear adjustment AGA [19][20][21][22]. AGA with linear adjustment of probability values cannot solve the problems of local optimal value and premature problem [19][20][21][22].…”

Section: Literature Review About Nonlinear Adjustment Agamentioning

confidence: 99%

Optimization Performance Analysis for Adaptive Genetic Algorithm with Nonlinear Probabilities

Sun¹,

Su²,

Yuan³

et al. 2022

IJACSA

View full text Add to dashboard Cite

Genetic Algorithm (GA) has been proven to be easy in falling into local optimal value due to its fixed crossover probability and mutation probability, while Adaptive Genetic Algorithm (AGA) has strong global search capability because the two probabilities adjust adaptively. There are two categories of AGA according to the different adjustment methods for crossover and mutation probabilities: probabilistic linear adjustment AGA and probabilistic non-linear adjustment AGA. AGA with linear adjustment of probability values cannot solve the problems of local optimal value and premature convergence. The nonlinear adaptive probability adjustment strategy can avoid premature convergence, poor stability and slow convergence speed. The typical AGA with nonlinear adjustment of probabilities are compared and analyzed through benchmark functions. The optimization performance of typical AGA algorithms is compared and analyzed by 10 benchmark functions. Compared with traditional GA and other AGA algorithms, AGA with crossover and mutation probabilities adjusted nonlinearly at both ends of the average fitness value has higher computational stability and is easy to find the global optimal solution, which provides ideas for the application of adaptive genetic algorithm.

show abstract

Study of Constellation Design of Pseudolites Based on Improved Adaptive Genetic Algorithm

Cited by 13 publications

References 8 publications

IPCB: Intelligent Pseudolite Constellation Based on High-Altitude Balloons

IPCB: Intelligent Pseudolite Constellation Based on High-Altitude Balloons

Parameter Optimization of Friction Pendulum Bearings Based on the Adaptive Genetic Algorithm Considering the Overall Evolutionary Status

Optimization Performance Analysis for Adaptive Genetic Algorithm with Nonlinear Probabilities

Contact Info

Product

Resources

About