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从工程应用出发,通过分析水下重力测量的各种误差源,建立精确的重力异常数据误差模型。改进了匹配算法,并设计了不同精度的一组惯性导航系统以验证重力辅助导航的可行性。结果表明,海洋固体潮、海潮、海面地形计算精度及厄特弗斯改正都是影响水下重力测量精度的主要因素,并行卡尔曼滤波算法能有效抑制非线性重力数据带来的滤波影响。
Based on the engineering application, the accurate gravity anomaly data error model is established by analyzing various error sources of underwater gravity measurement. The matching algorithm is improved and a set of inertial navigation systems with different accuracies are designed to verify the feasibility of gravity-assisted navigation. The results show that the ocean tide, ocean tide, surface topography calculation accuracy and Atterfuss correction are the main factors affecting the accuracy of underwater gravity measurement. Parallel Kalman filter algorithm can effectively suppress the influence of nonlinear gravity data.