Oceanic turbulence measurements made by an acoustic Doppler velocimeter (ADV) suffer from noise that potentially affects the estimates of turbulence statistics. This study examines the abilities of Kalman filtering and autoregressive moving average models to eliminate noise in ADV velocity datasets of laboratory experiments and offshore observations. Results show that the two methods have similar performance in ADV de-noising, and both effectively reduce noise in ADV velocities, even in cases of high noise. They eliminate the noise floor at high frequencies of the velocity spectra, leading to a longer range that effectively fits the Kolmogorov −5/3 slope at mid-range frequencies. After de-noising adopting the two methods, the values of the mean velocity are almost unchanged, while the root-mean-square horizontal velocities and thus turbulent kinetic energy decrease appreciably in these experiments. The Reynolds stress is also affected by high noise levels, and de-noising thus reduces uncertainties in estimating the Reynolds stress.

声学多普勒测速仪（ADV）进行的海洋湍流测量遭受的噪声可能会影响湍流统计数据的估计。这项研究检验了卡尔曼滤波和自回归移动平均模型消除实验室实验和海上观测的ADV速度数据集中的噪声的能力。结果表明，这两种方法在ADV降噪方面具有相似的性能，并且即使在高噪声情况下，两种方法都可以有效降低ADV速度的噪声。它们消除了高频频谱中的本底噪声，从而产生了一个更长的范围，可以有效地拟合中频处的Kolmogorov -5/3斜率。采用两种方法进行降噪后，平均速度值几乎保持不变，在这些实验中，均方根水平速度和湍动能明显降低。雷诺应力还受到高噪声水平的影响，因此降噪可减少估算雷诺应力的不确定性。