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The Argos-CLS Kalman Filter: Error Structures and State-Space Modelling Relative to Fastloc GPS Data

机译：Argos-CLS卡尔曼滤波器：与Fastloc GPS数据相关的误差结构和状态空间建模

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Understanding how an animal utilises its surroundings requires its movements through space to be described accurately. Satellite telemetry is the only means of acquiring movement data for many species however data are prone to varying amounts of spatial error; the recent application of state-space models (SSMs) to the location estimation problem have provided a means to incorporate spatial errors when characterising animal movements. The predominant platform for collecting satellite telemetry data on free-ranging animals, Service Argos, recently provided an alternative Doppler location estimation algorithm that is purported to be more accurate and generate a greater number of locations that its predecessor. We provide a comprehensive assessment of this new estimation process performance on data from free-ranging animals relative to concurrently collected Fastloc GPS data. Additionally, we test the efficacy of three readily-available SSM in predicting the movement of two focal animals. Raw Argos location estimates generated by the new algorithm were greatly improved compared to the old system. Approximately twice as many Argos locations were derived compared to GPS on the devices used. Root Mean Square Errors (RMSE) for each optimal SSM were less than 4.25km with some producing RMSE of less than 2.50km. Differences in the biological plausibility of the tracks between the two focal animals used to investigate the utility of SSM highlights the importance of considering animal behaviour in movement studies. The ability to reprocess Argos data collected since 2008 with the new algorithm should permit questions of animal movement to be revisited at a finer resolution.
机译：了解动物如何利用其周围环境需要准确地描述其在空间中的运动。卫星遥测是获取许多物种运动数据的唯一方法，但是数据容易出现不同程度的空间误差。状态空间模型（SSM）在位置估计问题上的最新应用提供了一种在表征动物运动时纳入空间误差的方法。最近，用于收集自由放养动物的卫星遥测数据的主要平台Service Argos提供了一种替代性的多普勒定位估计算法，该算法据称比以前的算法更精确，并且可以生成更多的位置。我们会根据自由放养动物的数据（相对于同时收集的Fastloc GPS数据）对这种新的估计过程性能进行全面评估。此外，我们测试了三种易于使用的SSM在预测两只焦点动物运动中的功效。与旧系统相比，新算法生成的原始Argos位置估算值得到了极大的改进。与使用的设备上的GPS相比，得出的Argos位置大约两倍。每个最佳SSM的均方根误差（RMSE）均小于4.25km，而某些产生的RMSE均小于2.50km。用于研究SSM效用的两种焦点动物之间的轨道生物学合理性差异突出了在运动研究中考虑动物行为的重要性。使用新算法对自2008年以来收集的Argos数据进行重新处理的功能应允许以更高分辨率解决动物运动的问题。

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