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. 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. Estimation accuracy varied among species with Root Mean Squared Errors usually <5 km and these decreased with increasing data sampling rate and precision of Argos locations. Including a model parameter to inflate Argos error ellipse sizes in the north - south direction resulted in more accurate location estimates. Finally, in some cases the model appreciably improved the accuracy of the Argos Kalman smoother locations, which should not be possible if the smoother is using all available information. Our model provides quality-controlled locations from Argos Least-Squares or Kalman filter data with accuracy similar to or marginally better than Argos Kalman smoother data that are only available via fee-based reprocessing. Simplicity and ease of use make the model suitable both for automated quality control of near real-time Argos data and for manual use by researchers working with historical Argos data.

中文翻译：

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一种连续时间状态空间模型，用于从动物传播的标签中对 argos 位置进行快速质量控制。

状态空间模型是质量控制和分析易出错动物运动数据的重要工具。Argos 卫星系统的近实时（24 小时内）能力可以通过通知动物何时进入风电场、航道和其他密集使用区域来帮助对人类活动进行动态海洋管理。这种能力还有助于在业务海洋预报模型中使用来自动物传播传感器的海洋观测。这种近乎实时的数据提供需要快速、可靠的质量控制来处理容易出错的 Argos 位置。我们制定了一个连续时间状态空间模型来过滤三种类型的 Argos 位置数据（最小二乘法、卡尔曼滤波器和卡尔曼平滑器），以考虑观察的不规则时间。我们的模型刻意简单，以确保自动化的速度和可靠性，Argos 位置数据的近实时质量控制。我们通过拟合从 7 个海洋脊椎动物的 61 个人收集的 Argos 位置来验证该模型，并将模型估计的位置与同时期的 GPS 位置进行比较。然后我们测试假设 Argos Kalman 滤波器/平滑误差椭圆是无偏的，并且 Argos Kalman 更平滑的定位精度不能通过后续的状态空间建模来提高。估计精度因物种而异，均方根误差通常小于 5 公里，并且随着数据采样率和 Argos 位置精度的增加而降低。包括一个模型参数以扩大南北方向的 Argos 误差椭圆大小，从而产生更准确的位置估计。最后，在某些情况下，该模型显着提高了 Argos Kalman 平滑位置的准确性，如果平滑器使用所有可用信息，这应该是不可能的。我们的模型从 Argos 最小二乘法或卡尔曼滤波器数据中提供质量控制的位置，其精度与 Argos 卡尔曼平滑数据相似或略好，这些数据只能通过基于费用的再处理获得。该模型简单易用，既适用于近乎实时的 Argos 数据的自动质量控制，也适用于处理历史 Argos 数据的研究人员手动使用。