Improving underwater localization accuracy with machine learning
Improving underwater localization accuracy with machine learning
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DOI：
10.1063/1.5012687
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发表时间：
2018-07-01
影响因子：
1.6
通讯作者：
Deng, Zhiqun Daniel
中科院分区：
文献类型：
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作者：
Rauchenstein, Lynn T.;Vishnu, Abhinav;Deng, Zhiqun Daniel
应用机器学习分类和回归算法来校准基于到达时间差 (TDOA) 的声学传感器阵列的定位误差，该阵列用于跟踪鲑鱼通过美国华盛顿州斯内克河上的水力发电大坝。首先使用近似最大似然算法跟踪固定和移动声学标签的位置。接下来，分类树的集合成功地识别和过滤了具有较大定位误差的数据点。此预过滤步骤允许创建机器学习回归模型函数，从而将固定轨道的中值距离误差降低了 50%，将移动轨道的中值距离误差降低了 34%。它还将之前距坝面（接收器）水平距离的亚米级定位精度范围从 100 m 扩展至 250 m。深度方向的中值距离误差尤其减小，固定轨道从0.49 m 下降到0.04 m，移动轨道从0.38 m 下降到0.07 m。这些方法可应用于任何具有稳定环境和阵列配置的基于 TDOA 的传感器网络中的误差校准。
Machine learning classification and regression algorithms were applied to calibrate the localization errors of a time-difference-of-arrival (TDOA)-based acoustic sensor array used for tracking salmon passage through a hydroelectric dam on the Snake River, Washington, USA. The locations of stationary and mobile acoustic tags were first tracked using the approximate maximum likelihood algorithm. Next, ensembles of classification trees successfully identified and filtered data points with large localization errors. This prefiltering step allowed the creation of a machine-learned regression model function, which decreased the median distance error by 50% for the stationary tracks and by 34% for the mobile tracks. It also extended the previous range of sub-meter localization accuracy from 100 m to 250 m horizontal distance from the dam face (the receivers). Median distance errors in the depth direction were especially decreased, falling from 0.49 m to 0.04 m in the stationary tracks and from 0.38 m to 0.07 m in the mobile tracks. These methods would have application to the calibration of error in any TDOA-based sensor network with a steady environment and array configuration.