基于卡尔曼滤波和位置、速度校正的移动平台重力测量用于地层监测和地震、火山活动调查

A. Almasi
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引用次数: 1

摘要

重力测量可以响应地下密度和特征的变化,是一种非侵入性的、经济有效的地下识别和表征方法。重力测量是地震和火山活动调查中进行地层监测的有效工具。在移动平台上进行重力观测尤其重要,特别是在偏远和近海地区,通过飞机、船、船、潜艇、车辆、卫星等进行重力观测。由于难以从平台加速度、非线性、漂移和动态角运动中识别重力,测量变得复杂。提出了二阶卡尔曼滤波器的解决方案,二阶卡尔曼滤波器是一种应用于高度非线性测量问题的递归估计器。该滤波器最佳地结合了三轴陀螺仪、加速度计和平台位置和速度信号的数据,以提供精确的姿态和重力测量。大量的仿真验证了该方法在不同动态环境下从不同车辆进行测量的准确性和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Gravity measurement from moving platform by Kalman Filter and position and velocity corrections for earth layer monitoring to earthquake and volcano activity survey
Gravity measurement responds to changes in subsurface density and characteristics and is a non-invasive and cost effective way to identify and characterize subsurface. Gravity measurement is an effective tool for earth layer monitoring to earthquake and volcano activity survey. It is particularly important for gravity observation from moving platforms, especially for remote and offshore areas by aircraft, boat, ship, submarine, vehicle, satellite, etc. The measurement is complicated by difficulty to discern gravity from platform accelerations, nonlinearity, drift and dynamic angular movement. Presented solution is second order Kalman Filter, a recursive estimator that is applied to highly nonlinear measurement problems. The filter optimally combines data of three-axis gyros, accelerometers and platform position and velocity signals to provide accurate attitude and gravity measurement. Extensive simulations verified accuracy and robustness of proposed method for measurement from different vehicles in various dynamic environments.
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