Cooperative filtering for parameter identification of diffusion processes
Cooperative filtering for parameter identification of diffusion processes
复制标题
用于扩散过程参数识别的协同过滤
DOI：
10.1109/cdc.2016.7798925
复制
发表时间：
2016
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;Fumin Zhang;Wencen Wu
提出了一种利用移动传感器网络采集的数据在线辨识二维扩散过程参数的合作滤波方法。扩散方程被结合到与移动传感器的轨迹相关联的信息动力学中。提出了一种协作式卡尔曼滤波器，以提供场值、梯度和场值沿轨迹的时间变化的估计。这导致了一种不同于使用静态传感器的扩散过程状态估计和参数辨识的联合设计方案。利用滤波器的状态估计，设计了一种递推最小二乘(RLS)算法来估计场的未知扩散系数。给出了合作卡尔曼滤波收敛的一组充分条件。仿真结果表明，该方法具有较好的性能。
This paper presents a cooperative filtering scheme for online parameter identification of 2D diffusion processes using data collected by a mobile sensor network moving in the diffusion field. The diffusion equation is incorporated into the information dynamics associated with the trajectories of the mobile sensors. A cooperative Kalman filter is developed to provide estimates of field values, the gradient, and the temporal variations of the field values along the trajectories. This leads to a co-design scheme for state estimation and parameter identification for diffusion processes that is different from using static sensors. Utilizing the state estimates from the filters, a recursive least square (RLS) algorithm is designed to estimate the unknown diffusion coefficient of the field. A set of sufficient conditions is derived for the convergence of the cooperative Kalman filter. Simulation results show satisfactory performance of the proposed method.