A new fast satellite selection algorithm for BDS-GPS receivers

Meng, Fanchen; Zhu, Biao; Wang, Shan

doi:10.1109/sips.2013.6674535

Cited by 12 publications

(6 citation statements)

References 11 publications

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“…In practical applications, we need to use the navigation messages of satellites to compute the position of the receiver. But if all observable satellites are used to compute the solution, the receiver will encounter extremely burdensome pressure due to large computational complexity [5]. In order to guarantee positioning precision while reducing computation load, it is necessary to select the right satellites subset from observable ones for solving problem in the various applications mentioned above.…”

Section: Introductionmentioning

confidence: 99%

A Navigation Satellites Selection Method Based on ACO With Polarized Feedback

Hong

Xia

et al. 2020

IEEE Access

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Selecting the optimal satellite subset for positioning from all satellites in view can not only achieve positioning accuracy but also reduce the computational burden. In this paper, a navigation satellites selection method is proposed based on ant colony optimization with the improvement of polarized feedback (ACO-PF). Firstly, the satellite selection problem is described as a combinatorial optimization problem, and the noise weighted geometric dilution of precision (NWGDOP) is defined as the criterion for satellite selection. Then the ant colony optimization (ACO) is incorporated to solve the problem, and a polarized feedback mechanism is presented to improve the convergence speed of algorithm. Meanwhile, a perturbation operator is designed to improve the global searching ability of the algorithm. The numerical experimental results show that ACO-PF can select the superior satellites combination which provides highprecision positioning. And its convergence outperforms the related algorithms by up to 50%. Besides, the achieved NWGDOP of ACO-PF is usually 0.065 smaller than ACO. Therefore, the ACO-PF method can be considered as a promising candidate for satellite selecting in navigation applications.INDEX TERMS Ant colony optimization (ACO), noise weighted, geometric dilution of precision (GDOP), polarized feedback, perturbation operator

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Section: Introductionmentioning

confidence: 99%

A Navigation Satellites Selection Method Based on ACO With Polarized Feedback

Hong

Xia

et al. 2020

IEEE Access

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“…If a satellite combination with good spatial distribution is selected quickly from all visible satellite combinations, the number of GDOP calculations could be reduced, and the time-consuming required for satellite selection could be reduced. 3,4 Therefore, it is necessary to select the right satellite subset from observable ones for solving the problem in the various applications mentioned above. The problem of satellite selection has attracted many academicians to study.…”

Section: Introductionmentioning

confidence: 99%

A navigation satellite selection algorithm for optimized positioning based on Gibbs sampler

Xia

Zhi

et al. 2020

International Journal of Distributed Sensor Networks

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In various applications of satellite navigation and positioning, it is a key topic to select suitable satellites for positioning solutions to reduce the computational burden of the receiver in satellite selection system. Moreover, in order to reduce the processing burden of receivers, the satellite selection algorithm based on Gibbs sampler is proposed. First, the visible satellites are randomly sampled and divided into a group. The group is regarded as an initial combination selection scheme. Then, the geometric dilution of precision is chosen as an objective function to evaluate the scheme’s quality. In addition, the scheme is updated by the conditional probability distribution model of the Gibbs sampler algorithm, and it gradually approaches the global optimal solution of the satellite combination with better geometric distribution of the space satellite. Furthermore, an “adaptive perturbation” strategy is introduced to improve the global searching ability of the algorithm. Finally, the extensive experimental results demonstrate that when the number of selected satellite is more than 6, the time that the proposed algorithm with the improvement of “adaptive perturbation” takes to select satellite once is 43.7% of the time that the primitive Gibbs sampler algorithm takes. And its solutions are always 0.1 smaller than the related algorithms in geometric dilution of precision value. Therefore, the proposed algorithm can be considered as a promising candidate for satellite navigation application systems.

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“…In its straightforward formulation, this problem is non-convex and is hard for a solution in real time. Approximate solution of this problem are considered in [9], [16], [8], [11], [7], [3].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Usage of SDP Relaxation in Some GNSS Navigation Problems

Rapoport¹

2019

dtcse

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In this paper, we propose an approach to the solution of two optimization problems that arise when solving problems of satellite navigation. The first problem consists in attitude determination using carrier phase and code range observables measured by multiple GNSS antennas. In general, the attitude determination is the non-convex optimization problem. The second problem consists in the optimization of the set of the Global Navigation Satellite System (GNSS) signals chosen for processing when solving the positioning problem. It is the binary optimization problem which is hard for a solution in real time. We show how these problems can be reduced to the convex semidefinite programming problem (SDP) and, therefore, efficiently solved.

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A new fast satellite selection algorithm for BDS-GPS receivers

Cited by 12 publications

References 11 publications

A Navigation Satellites Selection Method Based on ACO With Polarized Feedback

A Navigation Satellites Selection Method Based on ACO With Polarized Feedback

A navigation satellite selection algorithm for optimized positioning based on Gibbs sampler

Usage of SDP Relaxation in Some GNSS Navigation Problems

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