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INTEGRATION OF A STRAPDOWN GRAVIMETER SYSTEM IN AN AUTONOMOUS UNDERWATER VEHICLE

机译:在自主水下车辆中的镶嵌重量计系统的整合

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We present a new mobile instrument for measuring dynamically the gravity vector and its gradients in underwater environment, named GRAVIMOB. Our instrument is a strapdown sensor, consisted of electrostatic accelerometers installed in a waterproof sphere. It is designed to be embedded in an Autonomous Underwater Vehicle (AUV). Since the positioning of an AUV is approximate in underwater environment, the key issue raised here is to estimate the uncertainty in the gravity field resulting from the use of such position data. This paper focuses on the assessment of the system performances. The latter have been studied by simulation with reference data calculated from actual submarine geological structures, on which different noise models have been added. Results show that spatial evolutions of the gravity field and statistical properties of stochastic processes affecting the measurements have to be considered carefully in order the minimize the error. The Unscented Kalman Filter (UKF) has been favored to the Extended Kalman Filter (EKF) by its ease of implementation and its better robustness to non-linearities.
机译:我们提供了一种新的移动仪器,用于测量动态的重力矢量及其在水下环境中的梯度,名为GAGIMOB。我们的仪器是一个挂件传感器,由安装在防水​​球中的静电加速度计组成。它设计用于嵌入自动水下车辆(AUV)中。由于AUV的定位在水下环境中是近似的,因此这里提出的关键问题是估计由这种位置数据的使用产生的重力场中的不确定性。本文重点介绍了对系统性能的评估。已经通过模拟使用从实际潜艇地质结构计算的参考数据进行了研究,其中添加了不同的噪声模型。结果表明,重力场的空间演进和影响测量的随机过程的统计特性必须仔细考虑最小化误差。无需的卡尔曼滤波器(UKF)通过其易于实施以及对非线性的更好的稳健性来获利到扩展卡尔曼滤波器(EKF)。

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