Improved Estimation of ADCP Apparent Bed-Load Velocity Using a Real-Time Kalman Filter

An estimate of apparent bed-load velocity $\left(v\right)$ can be derived from the difference between differential global positioning system (DGPSs) and acoustic Doppler current profiler (ADCP) bottom track (BT) measurements when BT is biased by a 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 (Rennie and Rainville, 2008, J. Hydraulic Eng., in review). The boat velocity estimated using the Kalman filter is superior to boat velocity from raw GPS data. In this paper we assess the improvement in estimation of $v$ using the Kalman filter as opposed to raw GPS data. Specifically, a synthetic moving bed bias was generated for 22 repeat transects of the Gatineau River, Quebec. The synthetic moving bed bias had mean, variance, and distribution across the section as typically observed during bed-load transport conditions, and had the advantage that it was known explicitly. The errors in estimated apparent bed-load 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 $v$ , (e.g., 61% reduction in error when the Kalman filter boat velocity was used instead of wide area augmentation system GGA), because boat velocity errors were reduced. Tests with real moving bed data confirmed the Kalman filter was able to significantly reduce errors in bed load calculated with stand alone GPS.