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Multi-Scenario Variable-State Robust Fusion Algorithm for Ranging Analysis Framework
DOI:10.3390/agriculture14040516.png)
摘要
En 中文
Integrating modern information technology with traditional agriculture has made agricultural machinery navigation essential in PA (precision agriculture). However, agricultural equipment faces challenges such as low positioning accuracy and poor algorithm adaptability due to the complex farmland environment and various operational requirements. In this research, we proposed a generalized ranging theoretical framework with multi-scenario variable-state fusion to improve the GNSS (Global Navigation Satellite System) observation exchange performance among agricultural vehicles, and accurately measure IVRs (inter-vehicular ranges). We evaluated the effectiveness of three types of GNSS observations, including PPP-SD (precise single point positioning using single difference), PPP-TCAR (precise single point positioning using double difference based on three-carrier ambiguity resolution), and PPP-LAMBDA (precise single point positioning using double difference based on least-squares ambiguity decorrelation adjustment). Moreover, we compared the accuracy of IVRs measurements. Our framework was validated through field experiments in different scenarios. It provides insights into the appropriate use of different positioning algorithms based on the application scenario, application objects, and motion states.
Keyword:
precision agriculture
GNSS
theoretical framework
precise single point positioning
inter-vehicular ranges
期刊
IF:
3.6
论文数:
1.3W
被引数:
2.8W
机构
暂无机构信息
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