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<p>Over the past decade, more global navigation satellite system (GNSS) satellites have led to the development of receivers that support multiple constellations. Advances in receiver technology to support multi-GNSS constellations require more power and resources, making them unsuitable for budget-friendly wearables and smartphones. For multi-GNSS constellations, geometric dilution of precision (GDOP) is a key measurement unit selection parameter. Identifying the optimal subset requires an exhaustive search since the GDOP performance criterion is both non-linear and non-separable, making direct analytical solutions impractical. Traditional matrix inverse GDOP calculation requires several operations that use more resources and power. This paper revisits GDOP parameterization in multi-GNSS constellations and proposes an efficient swarm-based meta-heuristic approach to filter satellites using GDOP without compromising receiver performance accuracy. The proposed method optimizes computation by selecting the best satellites for position calculation. Multi-GNSS (GPS and NAVIC) cases show that the proposed method is 4–7 times faster and 72–87% more efficient than the traditional traversal method.</p>

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Artificial bee colony-based satellite selection for multi-constellation GNSS receiver

  • 期刊论文

  • 发表日期：

    2026年3月19日

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  • Prateek Singh,
  • Nitin Sharma

摘要

Over the past decade, more global navigation satellite system (GNSS) satellites have led to the development of receivers that support multiple constellations. Advances in receiver technology to support multi-GNSS constellations require more power and resources, making them unsuitable for budget-friendly wearables and smartphones. For multi-GNSS constellations, geometric dilution of precision (GDOP) is a key measurement unit selection parameter. Identifying the optimal subset requires an exhaustive search since the GDOP performance criterion is both non-linear and non-separable, making direct analytical solutions impractical. Traditional matrix inverse GDOP calculation requires several operations that use more resources and power. This paper revisits GDOP parameterization in multi-GNSS constellations and proposes an efficient swarm-based meta-heuristic approach to filter satellites using GDOP without compromising receiver performance accuracy. The proposed method optimizes computation by selecting the best satellites for position calculation. Multi-GNSS (GPS and NAVIC) cases show that the proposed method is 4–7 times faster and 72–87% more efficient than the traditional traversal method.