Possible Models for Combining Tracking Data with Conventional Tagging Data
Possible Models for Combining Tracking Data with Conventional Tagging Data
复制标题
将跟踪数据与传统标签数据相结合的可能模型
DOI：
10.1007/978-94-017-1402-0_24
复制
发表时间：
2001
期刊：
影响因子：
--
通讯作者：
D. Fournier
中科院分区：
文献类型：
--
作者：
J. Sibert;D. Fournier
对流-扩散模型已成功地用于描述用传统省道标签标记的金枪鱼重新捕获的时间和地点。这种模型是有偏随机游走的连续类比。本文演示了如何使用有偏随机游走来模拟由档案标签记录的金枪鱼的大规模移动，从而捕捉到轨迹的所有主要特征。有偏随机游走模型的参数与平流扩散模型的参数相同，表明联合参数估计方法可能是可行的。最后讨论了卡尔曼滤波在跟踪数据分析中的潜在应用。该统计模型有可能提高来自跟踪设备的地理位置估计的准确性，以及从跟踪数据估计有偏的随机游动参数。
Advection-diffusion models have been used successfully to describe the time and place of recapture of tuna tagged with conventional dart tags. Such models are the continuous analogs of a biased random walk. This paper demonstrates how biased random walks can be used to simulate large scale movements of tunas as recorded by archival tags in a way that captures all of the major characteristics of the tracks. The parameters of the biased random walk model are identical to the parameters of the advection diffusion model, suggesting that a joint parameter estimation procedure might be feasible. Finally, the potential application of the Kalman filter to the analysis of tracking data is discussed. This statistical model has the potential to increase the accuracy of geoposition estimates from tracking devices as well as to estimate biased random walk parameters from tracking data.