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<p>Accurate and reliable vehicle positioning during extended Global Navigation Satellite System/Inertial Navigation System outages remains challenging due to noise, outliers, and biases in input data. This study evaluates the robustness of machine learning (ML) models under two specific conditions: (1) noise and outliers present in training target data, and (2) errors and biases within input data. Two ML methods–Long Short-Term Memory (LSTM) and Gradient Boosting Decision Tree (GBDT)–were evaluated using the proposed methodology. Results indicate that both ML methods significantly enhance horizontal positioning accuracy compared to the conventional Kalman filter (KF). Specifically, LSTM reduces horizontal RMSE by approximately 85.8% (from 39.5 to 5.6&#xa0;m), while GBDT achieves a reduction of approximately 77.7% (to 8.8&#xa0;m). Additionally, the robustness analysis confirms that the proposed LSTM-based strategy demonstrates substantial robustness by maintaining consistent prediction accuracy, even in the presence of significant errors and outliers in training data, as well as being minimally impacted by errors and biases introduced in input variables.</p>

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Assessing the robustness of machine learning strategy for GNSS/INS vehicle positioning solutions enhancement

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月11日

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  • Wenzong Gao,
  • Yanming Feng

摘要

Accurate and reliable vehicle positioning during extended Global Navigation Satellite System/Inertial Navigation System outages remains challenging due to noise, outliers, and biases in input data. This study evaluates the robustness of machine learning (ML) models under two specific conditions: (1) noise and outliers present in training target data, and (2) errors and biases within input data. Two ML methods–Long Short-Term Memory (LSTM) and Gradient Boosting Decision Tree (GBDT)–were evaluated using the proposed methodology. Results indicate that both ML methods significantly enhance horizontal positioning accuracy compared to the conventional Kalman filter (KF). Specifically, LSTM reduces horizontal RMSE by approximately 85.8% (from 39.5 to 5.6 m), while GBDT achieves a reduction of approximately 77.7% (to 8.8 m). Additionally, the robustness analysis confirms that the proposed LSTM-based strategy demonstrates substantial robustness by maintaining consistent prediction accuracy, even in the presence of significant errors and outliers in training data, as well as being minimally impacted by errors and biases introduced in input variables.