This paper presents three batch estimation methods that use noisy ground velocity and heading measurements from a vehicle executing a circular orbit (or similar large heading change maneuver) to estimate the speed and direction of a steady, uniform, flow-field. The methods are based on a simple kinematic model of the vehicle's motion and use curve-fitting or nonlinear least-square optimization. A Monte Carlo simulation with randomized flow conditions is used to evaluate the batch estimation methods while varying the measurement noise of the data and the interval of unique heading traversed during the maneuver. The methods are also compared using experimental data obtained with a Bluefin-21 unmanned underwater vehicle performing a series of circular orbit maneuvers over a five hour period in a tide-driven flow.

翻译：本文提出了三种批量估计方法，利用执行圆形轨迹（或类似大航向变化机动）的飞行器所获取的含噪地面速度和航向测量值，来估计稳态均匀流场的流速与流向。这些方法基于飞行器运动的简单运动学模型，采用曲线拟合或非线性最小二乘优化技术。通过引入随机流场条件的蒙特卡罗仿真，在改变数据测量噪声及机动过程中唯一航向遍历区间的情况下，评估了上述批量估计方法的性能。此外，利用"蓝鳍-21"无人水下航行器在潮汐驱动流场中连续五小时执行系列圆形轨迹机动所获得的实验数据，对各类方法进行了对比验证。