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数据驱动的水下导航适配区分类预测
首发时间：2024-06-27
摘要：针对导航系统对于复杂的水下地质特征和地下重力异常分布如何进行准确识别和分类适配区成为亟待解决的问题。传统分类方法在处理地质和重力数据的复杂性方面存在局限性，导致预测结果不够准确和可靠。开发一个有效的区域适配区分类预测模型，辅助导航系统在不同水下区域中进行决策变得十分重要。选择重力场坡度标准差、重力场粗糙度和重力异常差异熵作为初始特征属性指标，进行pearson相关性系数分析，发现重力异常值与重力场坡度标准差以及重力场粗糙度之间存在高度相关性，因此选取这两个特征作为模型输入。提出了黄金正弦搜索策略、反向学习和柯西变异扰动优化后的麻雀搜索算法，并将其与xgboost集成学习器结合，构建基于优化麻雀搜索算法的xgboost适配区分类预测模型。优化过程使模型更好地适应复杂的水下地质特征和地下重力异常分布。结果表明，该模型能够有效地将不同水下区域分类为适配区或非适配区。模型在实际应用中的分类准确性显著提高，能为导航系统在复杂环境中提供更为可靠的决策支持。
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data-driven prediction of underwater navigation fitness zone classification
abstract：how to accurately identify and classify adaptive zones for complex underwater geological features and underground gravity anomaly distribution in navigation systems has become an urgent problem to be solved. traditional classification methods have limitations in dealing with the complexity of geological and gravity data, resulting in inaccurate and unreliable prediction results. developing an effective regional adaptation zone classification prediction model to assist navigation systems in making decisions in different underwater areas has become crucial. selecting the standard deviation of gravity field slope, gravity field roughness, and gravity anomaly difference entropy as initial feature attribute indicators, pearson correlation coefficient analysis was conducted. it was found that there is a high correlation between gravity anomaly values, gravity field slope standard deviation, and gravity field roughness. therefore, these two features were selected as model inputs. a golden sine search strategy, reverse learning, and cauchy mutation perturbation optimized sparrow search algorithm were proposed, and combined with the xgboost ensemble learner to construct an xgboost adaptation area classification prediction model based on the optimized sparrow search algorithm. the optimization process enables the model to better adapt to complex underwater geological features and underground gravity anomaly distribution. the results indicate that the model can effectively classify different underwater areas as adaptive or non adaptive zones. the classification accuracy of the model has significantly improved in practical applications, providing more reliable decision support for navigation systems in complex environments.
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