摘要
排放于水库湖泊中的污染物的扩散易受到边界影响.本文首先对静态水体中的近岸污染源扩散进行理论分析,提出了一种分段浓度模型.然后,研究了静态水体中靠近不透水边界的污染源定位问题,指出该问题中的未知参数不仅有污染源位置,还包括质量流率和初始扩散时间,分别给出了通用模型法、近似函数法、基于无迹卡尔曼滤波的估计方法求解参数估计问题.通用模型法与近似函数法分别通过求解基于原始扩散模型和分段扩散模型的约束非线性最小二乘算法获取参数估计.通用模型法可快速获取目标源相关信息,近似函数法有更稳健的参数估计性能,但需要经历多个采样时刻后才可执行.基于无迹卡尔曼滤波(Unscented Kalman filter,UKF)的估计方法结合扩散过程,可有效权衡数值计算复杂度与估计性能.在仿真实验部分,对近岸污染源扩散过程进行了水文模拟,根据模拟数据对比了不同算法的实验性能,说明了各算法的优势和不足.
In water environments such as water reservoirs and lakes, the diffusion of pollutants is affected by boundaries. Firstly, the offshore plume source diffusion in static water is analysed and a piecewise concentration model is proposed. The localization of the pollution source near an impervious boundary is studied. It is shown that unknown parameters include not only the source position but also the mass flow rate and the initial diffusion time. To estimate the unknown parameters, we provide three algorithms, which are respectively based on a general model, an approximation function and the unscented Kalman filter （UKF）. The first two algorithms employ the original concentration model and piecewise concentration model respectively, and estimate the parameters by solving the constrained nonlinear least squares problem. By using the general model based algorithm, source parameters can be acquired promptly. The algorithm based on the approximation function is more robust compared with that on the general model, although it can only be executed for sufficient samples. Considering the diffusion process, the algorithm based on UKF achieves a good tradeoff between the computation complexity and the estimation accuracy. The simulation data are generated by MODFLOW, which is a standard software for the hydrological simulation of source diffusion. Three proposed algorithms are tested by simulation data, and the results demonstrate their advantages and disadvantages.
出处
《自动化学报》
EI
CSCD
北大核心
2014年第5期849-861,共13页
Acta Automatica Sinica
基金
国家自然科学基金(60974012
61171160)
湖北省高等学校优秀中青年科技创新团队计划项目(T201302)资助~~
关键词
无线传感器网络
近岸污染源定位
无迹卡尔曼滤波
水文模拟
Wireless sensor networks, offshore pollution source localization, unscented Kalman filter （UKF）, hydrological simulation
