Optimal placement of mobile sensors for data assimilations
Optimal placement of mobile sensors for data assimilations
复制标题
用于数据同化的移动传感器的最佳放置
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Liang Xu
中科院分区：
文献类型：
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作者：
W. Kang;Liang Xu
我们探索了移动观测平台优化放置问题的理论框架以及相关算法，以最大限度地提高估计精度。本研究中的方法基于可观测性的概念，可观测性是对传感器数据和用户知识提供的信息的定量度量。为了找到最优的传感器位置，使用梯度投影法最大化可观测性。用Burgers方程来验证这种方法。为了证明传感器位置的最优性，采用基于两组数据的标准4D-Var算法进行了蒙特卡罗实验，一组数据来自等间距的传感器，另一组数据来自最优传感器位置。结果表明，相对于等间距传感器，当传感器放置在最优位置时，4D-Var数据同化的估计精度显著提高。还进行了稳健性研究，其中误差协方差矩阵变化50%，传感器噪声协方差变化100%。此外，传感器噪声和初始估计误差均采用高斯分布和均匀概率分布。在所有情况下，最佳传感器位置导致显著提高估计精度。
ABSTRACT We explore the theoretical framework as well as the associated algorithms for the problem of optimally placing mobile observation platforms to maximise the improvement of estimation accuracy. The approach in this study is based on the concept of observability, which is a quantitative measure of the information provided by sensor data and user-knowledge. To find the optimal sensor locations, the observability is maximised using a gradient projection method. The Burgers equation is used to verify this approach. To prove the optimality of the sensor locations, Monte Carlo experimentations are carried out using standard 4D-Var algorithms based on two sets of data, one from equally spaced sensors and the other from the optimal sensor locations. The results show that, relative to equally spaced sensors, the 4D-Var data assimilation achieves significantly improved estimation accuracy if the sensors are placed at the optimal locations. A robustness study is also carried out in which the error covariance matrix is varied by 50% and the sensor noise covariance is varied by 100%. In addition, both Gaussian and uniform probability distributions are used for the sensor noise and initial estimation errors. In all cases, the optimal sensor locations result in significantly improved estimation accuracy.