Cooperative parameter identification of advection-diffusion processes using a mobile sensor network
Cooperative parameter identification of advection-diffusion processes using a mobile sensor network
复制标题
使用移动传感器网络的平流扩散过程的协同参数识别
DOI：
10.23919/acc.2017.7963445
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Wencen Wu
中科院分区：
文献类型：
--
作者：
Jie You;Yufei Zhang;Mingchen Li;Kun Su;Fumin Zhang;Wencen Wu
利用移动传感器网络对平流扩散过程进行了在线参数辨识。提出了一种约束协同卡尔曼滤波器，用于估计移动传感器网络沿轨迹的场值和梯度，从而估计场值的时间变化。利用约束协同卡尔曼滤波的状态估计，设计了一种递推最小二乘算法来估计平流扩散过程的未知参数。本文对RLS进行了偏倚分析。除了在模拟的平流扩散场中验证所提出的算法外，我们还在实验室中建立了一个可控的二氧化碳平流扩散场，并设计了一个传感器网格来收集随时间变化的场浓度，以便在二氧化碳场中验证所提出的算法。实验结果证明了该算法在现实不确定性和干扰下的鲁棒性。
Online parameter identification of advection-diffusion processes is performed using a mobile sensor network. A constrained cooperative Kalman filter is developed to provide estimates of the field values and gradients along the trajectories of the mobile sensor network so that the temporal variations of the field values can be estimated. Utilizing the state estimates from the constrained cooperative Kalman filter, a recursive least square (RLS) algorithm is designed to estimate the unknown parameters of the advection-diffusion process. We provide bias analysis of the RLS in the paper. In addition to validating the proposed algorithm in simulated advection-diffusion fields, we build a controllable CO2 advection-diffusion field in a lab and design a sensor grid that collects the field concentration over time to allow the validation of the proposed algorithm in the CO2 field. Experimental results demonstrate robustness of the algorithm under realistic uncertainties and disturbances.