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捷联式航空重力测量系统的数据处理方法比较与分析

Comparison and Analysis of Data Processing Methods for Strapdown Aviation Gravity Survey System
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摘要 重力数据处理对获取高精度重力异常值有着重要作用,是重力测量的核心技术。重力仪在搭载运动载体进行重力测量时,载体的高频振动对重力测量数据和GPS数据均会产生不可避免的干扰,导致提取的重力异常粗值含有大量高频噪声。围绕重力数据的处理方法这一核心技术,介绍了FIR低通滤波、零相移滤波、标准Kalman滤波、正反Kalman滤波4种滤波方法的基本原理,运用这4种方法处理了SAG捷联式重力仪的某次实际飞行测量数据,比对了基于SINS/GPS组合导航和SINS/DGPS组合导航的重力测量数据处理结果。通过对本次试验重复测线内符合精度进行比对,验证了4种方法的可行性和优劣性,同时验证了SAG重力仪的测量精度。 Gravity data processing plays an important role in obtaining high-precision gravity anomaly values,and it is the core technology for gravity measurement.When the gravimeter is equipped with a motion carrier for gravity measurement,the high-frequency vibration of the carrier will be inevitably produced on gravity measurement data and GPS data.The interference causes the extracted gravity anomaly to contain a large amount of high frequency noise.This paper focuses on the core technology of gravity data processing method,the basic principles of FIR low-pass filtering,zero-phase shift filtering,standard Kalman filtering,and positive and negative Kalman filtering are introduced.Using these four methods,the actual flight measurement data of the SAG strapdown gravimeter is processed.The results of gravity measurement data processing based on SINS/GPS integrated navigation and SINS/DGPS integrated navigation are also compared.Thr-ough the comparison of the accuracy in the repeated survey lines of this test,the feasibility,advantages and disadvantages of the four methods are verified,and the measurement accuracy of the SAG gravimeter is also verified.
作者 修睿 郭刚 薛正兵 李东明 李海兵 XIU Rui;GUO Gang;XUE Zheng-bing;LI Dong-ming;LI Hai-bing(Pilot National Laboratoiy for Marine Science and Technology,Qingdao266237;Beijing Institute of Aerospace Control Devices,Beijing100039)
出处 《导航与控制》 2019年第3期65-73,104,共10页 Navigation and Control
基金 国家国际科技合作专项(编号:2014DFR80750) 国家重点研发计划(编号:2017YFC0601704) 青岛海洋科学与技术试点国家实验室主任基金(编号:QNLM201715)
关键词 捷联式重力仪 重力异常 重力数据处理 KALMAN滤波 低通滤波 strapdown gravimeter gravity anomaly gravity data processing Kalman filtering low-pass filtering
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