Optimization of a Kalman Filter for Improved aDcp Measurements Under High Real Bedload Conditions

摘要

An acoustic Doppler current profiler (aDcp) is an instrument commonly used to measure water velocities and discharge in rivers. When the river bed is mobile, Global Positioning System (GPS) is used to correct for bias in the aDcp bottom tracking signal. In this study, a real time Kalman filter is evaluated for its ability to give an improved estimate in aDcp measurements of boat velocity, apparent bedload velocity, boat position and water discharge compared to bottom tracking, Stand Alone GGA (i.e., position GPS) and Stand Alone VTG (i.e., velocity GPS). The filter is optimized using 9 river transects and is then tested on 11 transects, from 3 rivers with apparent bedload velocity rates greater than 2.5 cm/s. The results show the filter was able to produce the lowest mean boat velocity errors in 10 out of 11 transects compared to all other signals, and showed the greatest improvement in velocity estimates compared to Stand Alone GGA. The filter also significantly (a=0.05) lowered bedload velocity errors compared to Stand Alone GGA. The filter gave low errors in mean boat position, although position errors were close for all signals. Although the Kalman filter did not always produce the lowest discharge errors, it showed less variability compared to Stand Alone GGA. Overall, the performance of the filter appeared reasonable, and gave the advantage of producing a robust continuous signal when data from the input signals were missing.