A Hybrid EKF and Switching PSO Algorithm for Joint State and Parameter Estimation of Lateral Flow Immunoassay Models
A Hybrid EKF and Switching PSO Algorithm for Joint State and Parameter Estimation of Lateral Flow Immunoassay Models
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用于侧流免疫分析模型联合状态和参数估计的混合 EKF 和切换 PSO 算法
DOI：
10.1109/tcbb.2011.140
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发表时间：
2012-03
期刊：
影响因子：
--
通讯作者：
Xiaohui Liu
中科院分区：
文献类型：
--
作者：
曾念寅;Zidong Wang;李玉榕;杜民;Xiaohui Liu
在本文中，混合扩展卡尔曼滤波（EKF）和切换粒子群优化（SPSO）算法提出了联合估计的参数和状态的侧流免疫分析模型，通过可用的短时间序列测量。我们提出的方法推广了著名的EKF算法，通过对系统状态施加物理约束。请注意，状态约束在实际中经常遇到，这给系统分析和设计带来了相当大的困难。本文的主要目的是通过最大概率方法将扩展卡尔曼滤波和约束优化算法相结合来处理具有状态约束的动态建模问题。更具体地说，最近开发的SPSO算法是用来科普有约束的优化问题，通过添加一个惩罚项的目标函数，将其转换为无约束的优化问题。然后，该算法被用来同时识别侧流免疫分析模型的参数和状态。仿真结果表明，该算法比传统的EKF方法具有更好的性能。
In this paper, a hybrid extended Kalman filter (EKF) and switching particle swarm optimization (SPSO) algorithm is proposed for jointly estimating both the parameters and states of the lateral flow immunoassay model through available short time-series measurement. Our proposed method generalizes the well-known EKF algorithm by imposing physical constraints on the system states. Note that the state constraints are encountered very often in practice that give rise to considerable difficulties in system analysis and design. The main purpose of this paper is to handle the dynamic modeling problem with state constraints by combining the extended Kalman filtering and constrained optimization algorithms via the maximization probability method. More specifically, a recently developed SPSO algorithm is used to cope with the constrained optimization problem by converting it into an unconstrained optimization one through adding a penalty term to the objective function. The proposed algorithm is then employed to simultaneously identify the parameters and states of a lateral flow immunoassay model. It is shown that the proposed algorithm gives much improved performance over the traditional EKF method.
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