Real Time Estimation of ADCP Bedload Velocity Using a Kalman Filter

An estimate of apparent bedload velocity (ν) can be derived from the difference between Differential Global Positioning System (DGPS) and acoustic Doppler current profiler (aDcp) bottom track (BT) measurements when BT is biased by moving bottom. A Kalman filter has been developed to integrate GPS and bottom track data to improve estimation of boat velocity during ADCP measurements under moving bed conditions. The boat velocity estimated using the Kalman filter is superior to boat velocity from raw DGPS data. In this paper we assess the improvement in estimation of ν using the Kalman filter as opposed to raw DGPS data. Specifically, a synthetic moving bed bias was generated. The synthetic moving bed bias had mean, variance, and distribution across the section as typically observed during bedload transport conditions, and had the advantage that it was known explicitly. The errors in estimated apparent bedload velocity derived using either raw DGPS data or the Kalman filter boat velocity were compared. It was found that the improved boat velocity from the Kalman filter yielded significantly (alpha=0.05) better estimates of ν, because boat velocity errors were reduced.