With the improvement of resolution and precision of marine gravity anomaly data from satellite altimetry, the gravity matching navigation of underwater vehicles has been supported by background field data. To improve the accuracy, a multi-index model for analyzing the gravity background field characteristics is constructed, and an adaptive matching navigation algorithm is designed. First, the common single-index feature analysis methods are introduced, and their advantages and disadvantages are analyzed. The multi-index average statistical parameter (MASP) model applicable to the characterization of marine gravity anomaly background field is designed, and the reliability is verified by two satellite altimetric gravity anomaly datasets from the northwest Indian Ocean and the western Pacific Ocean. Then, an adaptive matching navigation algorithm is proposed by introducing the MASP model in the filtering process, which can adjust the filter structure in real time. Finally, the performance of the method is verified by about 30 h of field test. The matching results show that the proposed method can adjust the filter structure adaptively and has better comprehensive performance.

中文翻译：

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基于卫星测高海洋重力异常特征分析的自适应重力辅助惯性导航


随着卫星测高海洋重力异常数据分辨率和精度的提高，水下航行器的重力匹配导航得到了背景现场数据的支持。为了提高精度，构建了重力背景场特征分析的多指标模型，并设计了自适应匹配导航算法。首先介绍了常见的单指标特征分析方法，并分析了它们的优缺点。设计了适用于海洋重力异常背景场表征的多指标平均统计参数（MASP）模型，并通过西北印度洋和西太平洋两颗卫星高程重力异常数据集验证了模型的可靠性。然后，在滤波过程中引入MASP模型，提出一种自适应匹配导航算法，可以实时调整滤波器结构。最后通过约30 h的现场测试验证了该方法的性能。匹配结果表明，该方法能够自适应调整滤波器结构，具有较好的综合性能。