A continuous-time state-space model for rapid quality control of argos locations from animal-borne tags
A continuous-time state-space model for rapid quality control of argos locations from animal-borne tags
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DOI：
10.1186/s40462-020-00217-7
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发表时间：
2020-07-17
期刊：
影响因子：
4.1
通讯作者：
McMahon, Clive R.
中科院分区：
文献类型：
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作者：
Jonsen, Ian D.;Patterson, Toby A.;McMahon, Clive R.
背景状态空间模型是对易出错动物运动数据进行质量控制和分析的重要工具。Argos卫星系统的近实时(24小时内)能力可以通过在动物进入风力发电场、航道和其他密集型使用区时发出通知，帮助对人类活动进行动态海洋管理。这一能力还有助于在业务海洋预报模型中使用动物携带的传感器进行的海洋观测。这种近乎实时的数据提供需要快速、可靠的质量控制，以处理容易出错的Argos位置。方法建立一个连续时间的状态空间模型，对三种类型的Argos定位数据(最小二乘法、卡尔曼滤波法和卡尔曼平滑法)进行滤波，考虑到观测时间的不规则性。我们的模型故意简单，以确保Argos位置数据的自动化、接近实时的质量控制的速度和可靠性。我们通过对从7种海洋脊椎动物的61个个体收集的Argos位置的拟合来验证模型，并将模型估计的位置与同期的GPS位置进行比较。然后，我们检验了Argos卡尔曼滤波/平滑误差椭圆是无偏的假设，以及Argos卡尔曼平滑定位精度不能通过随后的状态空间建模来提高的假设。结果不同物种的估计精度不同，均方根误差较大
Background State-space models are important tools for quality control and analysis of error-prone animal movement data. The near real-time (within 24 h) capability of the Argos satellite system can aid dynamic ocean management of human activities by informing when animals enter wind farms, shipping lanes, and other intensive use zones. This capability also facilitates the use of ocean observations from animal-borne sensors in operational ocean forecasting models. Such near real-time data provision requires rapid, reliable quality control to deal with error-prone Argos locations. Methods We formulate a continuous-time state-space model to filter the three types of Argos location data (Least-Squares, Kalman filter, and Kalman smoother), accounting for irregular timing of observations. Our model is deliberately simple to ensure speed and reliability for automated, near real-time quality control of Argos location data. We validate the model by fitting to Argos locations collected from 61 individuals across 7 marine vertebrates and compare model-estimated locations to contemporaneous GPS locations. We then test assumptions that Argos Kalman filter/smoother error ellipses are unbiased, and that Argos Kalman smoother location accuracy cannot be improved by subsequent state-space modelling. Results Estimation accuracy varied among species with Root Mean Squared Errors usually