2022•PLoS ONEOpen access

Dynamic base stations selection method for passive location based on GDOP

Sheng Miao, Liang Dong, Jingyu Hou

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Abstract

For the problem of passive location in mobile cellular network, base stations (BSs) selection can improve positioning accuracy. Through the analysis of base station layout in cellular networks, using Geometric Dilution of Precision (GDOP) as the optimization objective, we propose a Dynamic Base Stations Selection (DBSS) method in a cellular unit. This method enables the system to dynamically select the positioning base station when positioning target in the detection area. DBSS mainly include three steps: nearest base station calculation, layout of base stations analysis, and base station selection based on the target location. We mainly focus on the derivation of four-base station dynamic selection (DBSS4) and five-base station dynamic selection (DBSS5) algorithms. In simulation experiments, DBSS4 algorithm and DBSS5algorithm were compared with the state-of-the-art of BSs selection methods. The results show that our proposed method can achieve the exhaustive search in cellular cells and reduce more than 20% of the GDOP cumulative positioning error compared with the fixed four-base station selection algorithm. Meanwhile, the proposed method is more efficient, requires less running time and floating-point operations (FLOPs) than other comparison algorithm, and is independent of localization algorithms.

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What this paper is about

For the problem of passive location in mobile cellular network, base stations (BSs) selection can improve positioning accuracy. Through the analysis of base station layout in cellular networks, using Geometric Dilution of Precision (GDOP) as the optimization objective, we propose a Dynamic Base Stations Selection (DBSS) method in a cellular unit. This method enables the system to dynamically select the positioning base station when positioning target in the detection area. DBSS mainly include three steps: nearest base station calculation, layout of base stations analysis, and base station selection based on the target location. We mainly focus on the derivation of four-base station dynamic selection (DBSS4) and five-base station dynamic selection (DBSS5) algorithms. In simulation experiments, DBSS4 algorithm and DBSS5algorithm were compared with the state-of-the-art of BSs selection methods. The results show that our proposed method can achieve the exhaustive search in cellular cells and reduce more than 20% of the GDOP cumulative positioning error compared with the fixed four-base station selection algorithm. Meanwhile, the proposed method is more efficient, requires less running time and floating-point operations (FLOPs) than other comparison algorithm, and is independent of localization algorithms.

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Available abstract

For the problem of passive location in mobile cellular network, base stations (BSs) selection can improve positioning accuracy. Through the analysis of base station layout in cellular networks, using Geometric Dilution of Precision (GDOP) as the optimization objective, we propose a Dynamic Base Stations Selection (DBSS) method in a cellular unit. This method enables the system to dynamically select the positioning base station when positioning target in the detection area. DBSS mainly include three steps: nearest base station calculation, layout of base stations analysis, and base station selection based on the target location. We mainly focus on the derivation of four-base station dynamic selection (DBSS4) and five-base station dynamic selection (DBSS5) algorithms. In simulation experiments, DBSS4 algorithm and DBSS5algorithm were compared with the state-of-the-art of BSs selection methods. The results show that our proposed method can achieve the exhaustive search in cellular cells and reduce more than 20% of the GDOP cumulative positioning error compared with the fixed four-base station selection algorithm. Meanwhile, the proposed method is more efficient, requires less running time and floating-point operations (FLOPs) than other comparison algorithm, and is independent of localization algorithms.

Key concepts: Dilution of precision, Base station, Selection (genetic algorithm), Computer science, Selection algorithm, Cellular network, Algorithm, Real-time computing

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