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This chapter constructs a rigorous theoretical framework for advanced stochastic modeling in real-time kinematic positioning (RTK). The discussion first introduces a variance and covariance component estimation method, where an efficient approach is also given. This technique meticulously quantifies measurement noise, ensuring that least squares adjustments yield unbiased estimates with minimal variance. Building on this foundation, the chapter then presents a fully-populated stochastic modeling paradigm designed to capture the complex interdependencies among observations. The chapter argues for approaches that address the unique constellation and signal characteristics of BeiDou Navigation Satellite System. Comprehensive experimental validations confirm that these tailored models significantly improve positioning reliability under diverse applications.

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Stochastic Modeling

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

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  • Bofeng Li,
  • Zhetao Zhang,
  • Weikai Miao

摘要

This chapter constructs a rigorous theoretical framework for advanced stochastic modeling in real-time kinematic positioning (RTK). The discussion first introduces a variance and covariance component estimation method, where an efficient approach is also given. This technique meticulously quantifies measurement noise, ensuring that least squares adjustments yield unbiased estimates with minimal variance. Building on this foundation, the chapter then presents a fully-populated stochastic modeling paradigm designed to capture the complex interdependencies among observations. The chapter argues for approaches that address the unique constellation and signal characteristics of BeiDou Navigation Satellite System. Comprehensive experimental validations confirm that these tailored models significantly improve positioning reliability under diverse applications.