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

Author

Listed:
  • Na Xia
  • Qinan Zhi
  • Menghua He
  • Yunqing Hong
  • Huazheng Du

Abstract

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.

Suggested Citation

  • Na Xia & Qinan Zhi & Menghua He & Yunqing Hong & Huazheng Du, 2020. "A navigation satellite selection algorithm for optimized positioning based on Gibbs sampler," International Journal of Distributed Sensor Networks, , vol. 16(6), pages 15501477209, June.
    • Handle: RePEc:sae:intdis:v:16:y:2020:i:6:p:1550147720929620
    DOI: 10.1177/1550147720929620
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    References listed on IDEAS

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    1. Geweke, John & Tanizaki, Hisashi, 2001. "Bayesian estimation of state-space models using the Metropolis-Hastings algorithm within Gibbs sampling," Computational Statistics & Data Analysis, Elsevier, vol. 37(2), pages 151-170, August.
    2. Jiancai Song & Guixiang Xue & Yanan Kang, 2016. "A Novel Method for Optimum Global Positioning System Satellite Selection Based on a Modified Genetic Algorithm," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-14, March.
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