Random Data: Analysis and Measurement Procedures
Random Data: Analysis and Measurement Procedures
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Autonomous vehicles (AVs) are commonly used in oceanic and more recently estuarine and riverine environments because they are small, versatile, efficient, moving platforms equipped with a suite of instruments for measuring environmental conditions. However, moving vessel observations, particularly those associated with Acoustic Doppler Current Profiler (ADCP) measurements, can be problematic owing to instrument noise, flow fluctuations, and spatial variability. A range of ADCPs manufactured by different companies were integrated on to an Unmanned Surface Vehicle (USV), an Unmanned Underwater Vehicle (UUV), and some additional stationary platforms and were deployed in a number of natural riverine and estuarine environments to evaluate the quality of the velocity profile over the depth, minimum averaging time interval requirements, and AV mission planning considerations. Measurements were obtained at fixed locations to eliminate any spatial variations in the mean flow characteristics. The USV has the unique capability to station-keep to within 1 m owing to its dual-propeller design, providing the best setup for spatially mapping velocity profiles. Single-propeller UUVs can perform a quasi-station-keeping ( 1 m/s. An appropriate averaging window, T *, was determined using the Kalman Algorithm with a Kalman gain equal to 1%. T * was found to be independent of depth, flow velocity, and environment. There was no correlation (R 2 = 0.18) for T * between flow magnitude and direction. Results from all measurements had a similar T * of approximately 3 min. Based on this, an averaging window of 4 min is conservatively suggested to obtain a statistically confident measure of the mean velocity profile.