Improving Precision in the Reference Velocity of ADCP Measurements Using a Kalman Filter with GPS and Bottom Track
Improving Precision in the Reference Velocity of ADCP Measurements Using a Kalman Filter with GPS and Bottom Track
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DOI：
10.1061/(asce)0733-9429(2008)134:9(1257
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发表时间：
2008-09
影响因子：
2.4
通讯作者：
C. Rennie;F. Rainville
中科院分区：
文献类型：
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作者：
C. Rennie;F. Rainville
全球定位系统（GPS）数据被用来测量船的速度在声学多普勒海流剖面仪（ADCP）流量测量，特别是当底部跟踪（BT）是由移动床偏置。卡尔曼滤波器的开发，以改善速度基准的ADCP在这种情况下使用。卡尔曼滤波是一种递归统计技术，它在给定各种输入及其方差的情况下估计过程的当前状态。在ADCP获得的数据的情况下，两个独立的速度测量和位置测量的可用性使得这种方法特别有吸引力。新的卡尔曼滤波器将GPS位置（GGA）和多普勒速度（VTG）的原始输入与BT数据在真实的时间内相结合，以产生最佳的速度估计。该技术的评估和校准使用各种精度的GPS数据同时收集沿着与无偏BT数据在两个不同的网站。在加蒂诺河，实时动态和广域增强系统校正.
Global positioning system (GPS) data are used to measure boat velocity during acoustic Doppler current profiler (ADCP) discharge measurements, particularly when bottom tracking (BT) is biased by moving bed. A Kalman filter is developed to improve the velocity reference used by the ADCP under such conditions. Kalman filtering is a recursive statistical technique that estimates the current state of a process, given various inputs and their variance. In the case of data obtained by ADCP, the availability of two independent velocity measurements and a position measurement makes this method particularly attractive. The new Kalman filter combines raw inputs for GPS position (GGA) and Doppler velocity (VTG) with BT data in real time to produce best estimates of velocity. The technique is evaluated and calibrated using various accuracies of GPS data collected simultaneously along with unbiased BT data at two different sites. On the Gatineau River, real-time kinematic and wide area augmentation system corrections ...